IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v85y2026ics1062940826000793.html

Investor sentiment and green finance indicators: exploring herding behavior in clean versus dirty cryptocurrencies

Author

Listed:
  • Pham, Dung Thi Ngoc

Abstract

This study examines whether investor sentiment and green finance affect herding in cryptocurrency markets through common or segment specific channels. Using a Quantile on Quantile Regression framework, we show that herding is structurally segmented across Clean and Dirty cryptocurrencies. In the Clean segment, the most coherent channels are Twitter based sentiment and broad sustainability benchmarks, particularly the Twitter Happiness Index and ESG Index, and their effects are associated more with relative repricing than with broad based convergence. In the Dirty segment, the more informative channels are fear based sentiment and tradable green market indicators, especially the Fear and Greed Index, the Global Wind Energy Index, and the Green Bond ETF, which are more closely associated with synchronized trading in states already susceptible to herding. A range of robustness tests preserves this qualitative structure. The findings indicate that sentiment and sustainability effects in cryptocurrency markets are nonlinear, state dependent, and portfolio specific.

Suggested Citation

  • Pham, Dung Thi Ngoc, 2026. "Investor sentiment and green finance indicators: exploring herding behavior in clean versus dirty cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:ecofin:v:85:y:2026:i:c:s1062940826000793
    DOI: 10.1016/j.najef.2026.102657
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940826000793
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2026.102657?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

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • 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:eee:ecofin:v:85:y:2026:i:c:s1062940826000793. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    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.