IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v59y2023i10p3378-3389.html
   My bibliography  Save this article

A Trading Strategy Based on Analysts’ Industry Analyses – Evidence from Textual Analyses of Analyst Reports in Chinese Stock Market

Author

Listed:
  • Lunwen Wu
  • Di Gao
  • Wanxuan Su
  • Dawei Liang
  • Qianqian Du

Abstract

Applying a deep-learning method that is efficient in differentiating the order of words, we extract the tone of analysts’ industry analyses to measure analyst expertise and aggregate it at industry level as OPNI. Based on OPNI, we successfully construct the industry hedging portfolio that longs industries with highest OPNI and shorts industries with lowest OPNI, which generates significant and robust abnormal returns. Furthermore, we find that the industry hedging portfolio based on industry-level numerical forecasts cannot generate significant returns. Additionally, in mechanism analyses, we find that the informativeness of analysts’ industry analyses is driven by its predictability on industry-level unexpected revenues and earnings. Our findings suggest that industry analyses in analysts’ reports contain incremental value about their industry expertise, which is beyond analysts’ quantitative forecasts.

Suggested Citation

  • Lunwen Wu & Di Gao & Wanxuan Su & Dawei Liang & Qianqian Du, 2023. "A Trading Strategy Based on Analysts’ Industry Analyses – Evidence from Textual Analyses of Analyst Reports in Chinese Stock Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(10), pages 3378-3389, August.
    • Handle: RePEc:mes:emfitr:v:59:y:2023:i:10:p:3378-3389
    DOI: 10.1080/1540496X.2023.2218968
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2023.2218968
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1540496X.2023.2218968?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    More about this item

    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:mes:emfitr:v:59:y:2023:i:10:p:3378-3389. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/MREE20 .

    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.