IDEAS home Printed from https://ideas.repec.org/a/wsi/tijaxx/v54y2019i02ns109440601950001x.html
   My bibliography  Save this article

Aggregate Accounting Data and the Prediction of Credit Risk

Author

Listed:
  • Dimitrios V. Kousenidis

    (School of Economics, Aristotle University of Thessaloniki, Thessaloniki, Greece)

  • Anestis C. Ladas

    (Department of Accounting and Finance, University of Macedonia, Thessaloniki, Greece)

  • Christos I. Negkakis

    (Department of Accounting and Finance, University of Macedonia, Thessaloniki, Greece)

Abstract

A recent area of accounting research concerns the ability of changes in aggregate earnings to predict various macroeconomic fundamentals. We extend this line of research and examine whether changes in aggregate earnings convey information regarding the changes in future sovereign risk, which is considered a proxy for changes in aggregate risk. The results of the study indicate that aggregate earnings changes have predictive ability for sovereign credit risk. This result persists even after controlling for liquidity and indicators of macroeconomic imbalance, as well as under alternative research specifications. We also find that in some of the research specifications, future sovereign credit risk is affected by earnings management. In general, our results point towards the presence of significant information in earnings that is related to future sovereign risk.

Suggested Citation

  • Dimitrios V. Kousenidis & Anestis C. Ladas & Christos I. Negkakis, 2019. "Aggregate Accounting Data and the Prediction of Credit Risk," The International Journal of Accounting (TIJA), World Scientific Publishing Co. Pte. Ltd., vol. 54(01), pages 1-30, March.
  • Handle: RePEc:wsi:tijaxx:v:54:y:2019:i:02:n:s109440601950001x
    DOI: 10.1142/S109440601950001X
    as

    Download full text from publisher

    File URL: https://www.worldscientific.com/doi/abs/10.1142/S109440601950001X
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S109440601950001X?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 search for a different version of it.

    Other versions of this item:

    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:wsi:tijaxx:v:54:y:2019:i:02:n:s109440601950001x. 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: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/tija .

    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.