IDEAS home Printed from https://ideas.repec.org/a/oup/revfin/v23y2019i1p245-277..html
   My bibliography  Save this article

Does Information Acquisition Alleviate Market Anomalies? Categorization Bias in Stock Splits

Author

Listed:
  • Dongmin Kong
  • Chen Lin
  • Shasha Liu

Abstract

Using a unique proprietary account-level trading dataset in China, we investigate how active information acquisition alleviates price-based return comovement, a typical anomaly in stock splits. We find that: 1) individual trading drives the comovement and the trading correlation between split stocks and the low-price portfolio increases significantly after splits; 2) individuals can learn the firm fundamentals through information acquisition, which effectively alleviates their categorized bias; and 3) the role of information acquisition is more significant in environments characterized by greater uncertainty. Our results are robust to different specifications and alternative measures. Taken together, this paper emphasizes the important role of information acquisition in alleviating behavioral bias and improving decision-making.

Suggested Citation

  • Dongmin Kong & Chen Lin & Shasha Liu, 2019. "Does Information Acquisition Alleviate Market Anomalies? Categorization Bias in Stock Splits," Review of Finance, European Finance Association, vol. 23(1), pages 245-277.
  • Handle: RePEc:oup:revfin:v:23:y:2019:i:1:p:245-277.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rof/rfx028
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheema, Arbab K. & Eshraghi, Arman & Wang, Qingwei, 2023. "Macroeconomic news and price synchronicity," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 390-412.
    2. Chen, Fu & Huang, Zhi-xiong & Wang, Fang & Xie, Zongyu, 2022. "Can corporate social responsibility disclosure alleviate asset price volatility? Evidence from China," Economic Modelling, Elsevier, vol. 116(C).
    3. Wang, Kai & Li, Tingting & San, Ziyao & Gao, Hao, 2023. "How does corporate ESG performance affect stock liquidity? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    4. Chen, Xing & Diao, Xundi & Wu, Chongfeng, 2022. "Heterogeneous investor attention and post earnings announcement drift: Evidence from China," Economic Modelling, Elsevier, vol. 110(C).
    5. Kong, Dongmin & Ji, Mianmian & Liu, Shasha, 2022. "Does the mandatory disclosure of audit information affect analysts' information acquisition?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    6. Kong, Dongmin & Lin, Zhiyang & Wang, Yanan & Xiang, Junyi, 2021. "Natural disasters and analysts' earnings forecasts," Journal of Corporate Finance, Elsevier, vol. 66(C).
    7. Tao Chen & Andreas Karathanasopoulos & Stanley Iat-Meng Ko & Chia Chun Lo, 2020. "Lucky lots and unlucky investors," Review of Quantitative Finance and Accounting, Springer, vol. 54(2), pages 735-751, February.

    More about this item

    Keywords

    Information acquisition; Comovement; Investor trading behavior; Stock splits;
    All these keywords.

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:oup:revfin:v:23:y:2019:i:1:p:245-277.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/eufaaea.html .

    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.