IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2019i4p87-99.html
   My bibliography  Save this article

Predicting Banks’ Subordinated Bond Issuances

Author

Listed:
  • Jinyoung YU

    (College of Economics, Sungkyunkwan University, Seoul, Republic of Korea)

  • Doojin RYU

    (College of Economics, Sungkyunkwan University, Seoul, Republic of Korea)

Abstract

This study investigates the predictive determinants of banks’ subordinated bond issuances. We employ macroeconomic indices, market-specific factors, individual bank financial ratios, and bank performance indices to predict banks’ subordinated debt management decisionmaking processes. We use logistic and panel data regression approaches to identify the variables that significantly affect banks’ decision to issue subordinated bonds. The logistic analysis indicates that economic expansion and insolvency risk increase the probability of banks’ subordinated bond issuances, whereas profitability has no significant influence. Consistent with this result, the panel data analysis reveals that the economic growth and insolvency risk in the previous period positively forecast the growth rate of subordinated bonds in the next period. Considering bank-specific financial ratios, we find that banks with higher capital adequacy ratios and operating costs tend to increase the size of their subordinated bond holdings

Suggested Citation

  • Jinyoung YU & Doojin RYU, 2019. "Predicting Banks’ Subordinated Bond Issuances," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 87-99, December.
    • Handle: RePEc:rjr:romjef:v::y:2019:i:4:p:87-99
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef4_19/rjef4_2019p87-99.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Ryu, Doojin & Yu, Jinyoung, 2020. "Hybrid bond issuances by insurance firms," Emerging Markets Review, Elsevier, vol. 45(C).

    More about this item

    Keywords

    Business cycle; Fixed effects; Logit model; Prediction; Random effects; Subordinated bond;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    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:rjr:romjef:v::y:2019:i:4:p:87-99. 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.html .

    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.