IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v44y2026i3p1004-1017.html

Do Asset Prices Help Predict Inflation? Evidence from Individual Stock Prices

Author

Listed:
  • Yumeng Cui
  • Yongmiao Hong
  • Naijing Huang
  • Yicheng Wang

Abstract

This paper revisits the predictive power of asset prices for inflation, focusing on individual stock prices rather than aggregate indices. Using a large panel data of firm-level stock prices and applying machine learning techniques, we demonstrate that individual stock prices significantly enhance the accuracy of inflation forecasts, particularly over medium- to long-term horizons and during periods of high inflationary and deflationary pressure. Compared to composite and industry-level stock indices, other aggregate asset prices, and Fama-French factors, individual stock prices contain valuable heterogeneous information, offering richer insights for inflation forecasting. These findings provide new empirical support for macro-finance theory, affirming the predictive value of asset prices from a micro-level perspective.

Suggested Citation

  • Yumeng Cui & Yongmiao Hong & Naijing Huang & Yicheng Wang, 2026. "Do Asset Prices Help Predict Inflation? Evidence from Individual Stock Prices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 44(3), pages 1004-1017, July.
    • Handle: RePEc:taf:jnlbes:v:44:y:2026:i:3:p:1004-1017
    DOI: 10.1080/07350015.2026.2654899
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2026.2654899
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07350015.2026.2654899?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

    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:taf:jnlbes:v:44:y:2026:i:3:p:1004-1017. 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/UBES20 .

    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.