IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v25y2018i7p472-476.html
   My bibliography  Save this article

Does investor sentiment dynamically impact stock returns from different investor horizons? Evidence from the US stock market using a multi-scale method

Author

Listed:
  • Yonghong Jiang
  • Bin Mo
  • He Nie

Abstract

This article uses the investor sentiment index to investigate the Granger causality between investor sentiment and stock returns for the US economy using a multi-scale method. To focus on the local analysis of different investor horizons, bivariate empirical mode decomposition is used to decompose time series of investor sentiment and stock returns at different timescales. We employ the linear and nonlinear integrated Granger causality method to examine the causal relationship of decomposed series on similar timescales. The results indicate both strong bilateral linear and nonlinear causality between longer-term investor sentiment and stock returns. However, there is no strong evidence for correlation of stock returns and investor sentiment on shorter timescales.

Suggested Citation

  • Yonghong Jiang & Bin Mo & He Nie, 2018. "Does investor sentiment dynamically impact stock returns from different investor horizons? Evidence from the US stock market using a multi-scale method," Applied Economics Letters, Taylor & Francis Journals, vol. 25(7), pages 472-476, April.
  • Handle: RePEc:taf:apeclt:v:25:y:2018:i:7:p:472-476
    DOI: 10.1080/13504851.2017.1340558
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2017.1340558
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/13504851.2017.1340558?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.

    Citations

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


    Cited by:

    1. Gaoshan Wang & Guangjin Yu & Xiaohong Shen, 2020. "The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach," Complexity, Hindawi, vol. 2020, pages 1-11, December.
    2. Juan Meng & Bin Mo & He Nie, 2023. "The dynamics of crude oil future prices on China's energy markets: Quantile‐on‐quantile and casualty‐in‐quantiles approaches," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1853-1871, December.
    3. Lao, Jiashun & Nie, He & Jiang, Yonghong, 2018. "Revisiting the investor sentiment–stock returns relationship: A multi-scale perspective using wavelets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 420-427.

    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:apeclt:v:25:y:2018:i:7:p:472-476. 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/RAEL20 .

    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.