IDEAS home Printed from https://ideas.repec.org/a/mfa/journl/v29y2021i1p59-72.html
   My bibliography  Save this article

Noise Trader Risk-Evidence from China’s Stock Market

Author

Listed:
  • Liang Ye

    (School of Economics and Management, Xiamen University Malaysia, Malaysia.)

  • Yeng-May Tan

    (School of Economics and Management, Xiamen University Malaysia, Malaysia.)

Abstract

Research Question: This paper examines the prevalence of noise trading and volatility asymmetry in the Chinese stock market. Motivation: Noise trader risk is a pervasive risk in the world's stock markets. It is driven by emotions and run counter to market stability. Noise trading has its practical repercussions. Hence, it is imperative for policymakers and investors to understand the behaviour and causes of noise risk to enhance market efficiency and optimize the financial decision-making process. Although most studies have confirmed the existence of noise in China's stock market, the volatility response findings have been mixed. Besides, prior studies found that China's stock market's volatility response behaves differently from its Western counterparts. Idea: In an attempt to examine the asymmetrical volatility response over different market conditions, we build our study on Feng et al. (2014) but over a different market sentiment period. Additionally, we combine our quantitative research with qualitative analysis. Hence, our paper verifies the existence of noise trading in China's stock market and dissects the plausible rationales behind the findings, keeping China’s unique historical developments and market conditions in mind. Data: Our sample data comprises the daily Shanghai Stock Exchange (SHSE) A-share index between 2nd January 2014 to 1st July 2019. Methods: We first employ a variance ratio method to test for noise trading evidence and subsequently develop an EGARCH-M model to detect yield asymmetry in the SHSE A-share market. Findings: Our result suggests that noise trading is prevalent in China's stock market and that market returns are more volatile in the face of good news than bad news. Hence, our findings are similar to Chen and Huang (2002) but contradict Feng et al. (2014). We attribute our findings to the investor's irrational investment psychology and behaviour, such as the widespread "catch up and kill down" operations among the noise traders and the market’s deficiencies. Contributions: Hence, our results provide important indications to investors and policymakers to assess the market conditions and devise optimal strategies.

Suggested Citation

  • Liang Ye & Yeng-May Tan, 2021. "Noise Trader Risk-Evidence from China’s Stock Market," Capital Markets Review, Malaysian Finance Association, vol. 29(1), pages 59-72.
    • Handle: RePEc:mfa:journl:v:29:y:2021:i:1:p:59-72
    as

    Download full text from publisher

    File URL: https://www.mfa.com.my/wp-content/uploads/2021/07/v29_i1_a4_pg59-72.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    4. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    5. Baker, Malcolm & Stein, Jeremy C., 2004. "Market liquidity as a sentiment indicator," Journal of Financial Markets, Elsevier, vol. 7(3), pages 271-299, June.
    6. Kenneth D. West, 1988. "Bubbles, Fads, and Stock Price Volatility Tests: A Partial Evaluation," NBER Working Papers 2574, National Bureau of Economic Research, Inc.
    7. Binswanger Mathias, 1999. "Can Noise Traders Cause Persistent Deviations from Fundamental Values on the Stock Market? / Können Noise Trader langfristige Abweichungen der Aktienkurse von ihren Fundamentalwerten bewirken?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 219(5-6), pages 556-574, October.
    8. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    9. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    10. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    11. Lee, Charles M C & Shleifer, Andrei & Thaler, Richard H, 1991. "Investor Sentiment and the Closed-End Fund Puzzle," Journal of Finance, American Finance Association, vol. 46(1), pages 75-109, March.
    12. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    13. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Wenzhao & Su, Chen & Duxbury, Darren, 2022. "The conditional impact of investor sentiment in global stock markets: A two-channel examination," Journal of Banking & Finance, Elsevier, vol. 138(C).
    2. Ung, Sze Nie & Gebka, Bartosz & Anderson, Robert D.J., 2023. "Is sentiment the solution to the risk–return puzzle? A (cautionary) note," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    3. Baker, Malcolm & Wurgler, Jeffrey & Yuan, Yu, 2012. "Global, local, and contagious investor sentiment," Journal of Financial Economics, Elsevier, vol. 104(2), pages 272-287.
    4. Tim Bollerslev & Robert J. Hodrick, 1992. "Financial Market Efficiency Tests," NBER Working Papers 4108, National Bureau of Economic Research, Inc.
    5. Omar Esqueda & Yongli Luo & Dave Jackson, 2015. "The linkage between the U.S. “fear index” and ADR premiums under non-frictionless stock markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 541-556, July.
    6. Chau, Frankie & Deesomsak, Rataporn & Koutmos, Dimitrios, 2016. "Does investor sentiment really matter?," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 221-232.
    7. Kumari, Jyoti, 2019. "Investor sentiment and stock market liquidity: Evidence from an emerging economy," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 166-180.
    8. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    9. Han, Xing & Li, Youwei, 2017. "Can investor sentiment be a momentum time-series predictor? Evidence from China," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 212-239.
    10. Hu, May & Chao, Chi-Chur & Lim, Jin Hao, 2016. "Another explanation of the mutual fund fee puzzle," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 134-152.
    11. repec:zbw:bofism:2012_047 is not listed on IDEAS
    12. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021, January-A.
    13. George Emm Halkos & Theodore Krintas, 2006. "Behavioural and fundamental explanations of discounts on closed end funds: an empirical analysis," Applied Financial Economics, Taylor & Francis Journals, vol. 16(5), pages 395-404.
    14. Muhammad Zia Ur Rehman & Zain ul Abidin & Faisal Rizwan & Zaheer Abbas & Sajjad Ahmad Baig, 2017. "How investor sentiments spillover from developed countries to developing countries?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1309096-130, January.
    15. Taipalus, Katja, 2012. "Detecting asset price bubbles with time-series methods," Scientific Monographs, Bank of Finland, number 2012_047.
    16. Taipalus, Katja, 2012. "Detecting asset price bubbles with time-series methods," Bank of Finland Scientific Monographs, Bank of Finland, volume 0, number sm2012_047, July.
    17. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    18. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    19. Alexander S. Sangare, 2005. "Efficience des marchés : un siècle après Bachelier," Revue d'Économie Financière, Programme National Persée, vol. 81(4), pages 107-132.
    20. Delcoure, Natalya & Zhong, Maosen, 2007. "On the premiums of iShares," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 168-195, March.
    21. Pegah Dehghani & Ros Zam Zam Sapian, 2014. "Sectoral herding behavior in the aftermarket of Malaysian IPOs," Venture Capital, Taylor & Francis Journals, vol. 16(3), pages 227-246, July.

    More about this item

    Keywords

    Noise trading; variance ratio; volatility asymmetry; EGARCH-M; market efficiency; behavioural finance;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G40 - Financial Economics - - Behavioral Finance - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mfa:journl:v:29:y:2021:i:1:p:59-72. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Capital Market Review (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.