IDEAS home Printed from https://ideas.repec.org/a/taf/hbhfxx/v23y2022i2p210-229.html

Selective Attention in Exchange Rate Forecasting

Author

Listed:
  • Svatopluk Kapounek
  • Zuzana Kučerová
  • Evžen Kočenda

Abstract

We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs.

Suggested Citation

  • Svatopluk Kapounek & Zuzana Kučerová & Evžen Kočenda, 2022. "Selective Attention in Exchange Rate Forecasting," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(2), pages 210-229, May.
    • Handle: RePEc:taf:hbhfxx:v:23:y:2022:i:2:p:210-229
    DOI: 10.1080/15427560.2020.1865355
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/15427560.2020.1865355
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/15427560.2020.1865355?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 look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Khyati Kathuria & Nand Kumar, 2022. "Pandemic‐induced fear and government policy response as a measure of uncertainty in the foreign exchange market: Evidence from (a)symmetric wild bootstrap likelihood ratio test," Pacific Economic Review, Wiley Blackwell, vol. 27(4), pages 361-379, October.
    2. Martina Halouskov'a & Daniel Stav{s}ek & Mat'uv{s} Horv'ath, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Papers 2205.05985, arXiv.org, revised Aug 2022.
    3. Halousková, Martina & Stašek, Daniel & Horváth, Matúš, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 50(C).
    4. Zhang, Qisi & Frömmel, Michael & Baidoo, Edwin, 2024. "Donald Trump's tweets, political value judgment, and the Renminbi exchange rate," International Review of Financial Analysis, Elsevier, vol. 93(C).
    5. Dominik Svoboda & Svatopluk Kapounek & Peter Albrecht, 2025. "The Effects of Short Interest on the Likelihood of Short Squeeze," MENDELU Working Papers in Business and Economics 2025-104, Mendel University in Brno, Faculty of Business and Economics.

    More about this item

    JEL classification:

    • F33 - International Economics - - International Finance - - - International Monetary Arrangements and Institutions
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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:taf:hbhfxx:v:23:y:2022:i:2:p:210-229. 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/hbhf .

    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.