IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/90566.html
   My bibliography  Save this paper

The Factors Influence Credit Risk in Japan Banking Sector Specific for Kyoto Bank

Author

Listed:
  • Mohammad Azmi, Nur Syafikah Atirah

Abstract

This research paper is to the performance of credit risk in japan bank specific for Bank of Kyoto. The measurement is based on bank specific factor and macroeconomics factor. However, the finding result would determine whether both factors is correlated significant or uninfluenced.

Suggested Citation

  • Mohammad Azmi, Nur Syafikah Atirah, 2018. "The Factors Influence Credit Risk in Japan Banking Sector Specific for Kyoto Bank," MPRA Paper 90566, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:90566
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/90566/1/MPRA_paper_90566.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    2. Dechow, Patricia M. & Kothari, S. P. & L. Watts, Ross, 1998. "The relation between earnings and cash flows," Journal of Accounting and Economics, Elsevier, vol. 25(2), pages 133-168, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tianjiao Zhao & Xiang Xiao & Qinghui Dai, 2021. "Transportation Infrastructure Construction and High-Quality Development of Enterprises: Evidence from the Quasi-Natural Experiment of High-Speed Railway Opening in China," Sustainability, MDPI, vol. 13(23), pages 1-23, December.
    2. Palocsay, Susan W. & Stevens, Scott P. & Brookshire, Robert G. & Sacco, William J. & Copes, Wayne S. & Buckman, Robert F. & Smith, J. Stanley, 1996. "Using neural networks for trauma outcome evaluation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 369-386, September.
    3. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    4. Thomas A. Gilliam, 2021. "Detecting Real Activities Manipulation: Beyond Performance Matching," Abacus, Accounting Foundation, University of Sydney, vol. 57(4), pages 619-653, December.
    5. Wang, Weimin & (Frank) Wang, Xu, 2014. "Predicting earnings in a poor information environment," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(1), pages 46-58.
    6. Sudip Datta & Mai Iskandar‐Datta & Vivek Singh, 2014. "Opaque financial reports and R2: Revisited," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 10-17, January.
    7. Luo, Yan & Wang, Xiaohuan & Zhang, Chenyang & Huang, Wei, 2021. "Accounting-based downside risk and expected stock returns: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 78(C).
    8. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    9. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    10. Goncharov, Igor & Werner, Joerg R. & Zimmermann, Jochen, 2009. "Legislative demands and economic realities: Company and group accounts compared," The International Journal of Accounting, Elsevier, vol. 44(4), pages 334-362, December.
    11. Gul, Ferdinand A. & Cheng, Louis T.W. & Leung, T.Y., 2011. "Perks and the informativeness of stock prices in the Chinese market," Journal of Corporate Finance, Elsevier, vol. 17(5), pages 1410-1429.
    12. Tunyi, Abongeh A. & Ntim, Collins G. & Danbolt, Jo, 2019. "Decoupling management inefficiency: Myopia, hyperopia and takeover likelihood," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 1-20.
    13. Ahmad Almashaqbeh & Hasnah Shaari & Hijattulah Abdul-Jabbar, 2019. "The Effect of Board Diversity on Real Earnings Management: Empirical Evidence From Jordan," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(5), pages 495-508, August.
    14. Gil Sadka, 2007. "Understanding Stock Price Volatility: The Role of Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 45(1), pages 199-228, March.
    15. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
    16. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    17. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
    18. Takashi Obinata, 2002. "Concept and Relevance of Income," CIRJE F-Series CIRJE-F-171, CIRJE, Faculty of Economics, University of Tokyo.
    19. Alexandre Garel & Jose Martin-Flores & Arthur Petit-Romec & Ayesha Scott, 2021. "Institutional investor distraction and earnings management," Post-Print hal-03096196, HAL.
    20. Igor Goncharov & Martin Jacob, 2014. "Why Do Countries Mandate Accrual Accounting for Tax Purposes?," Journal of Accounting Research, Wiley Blackwell, vol. 52(5), pages 1127-1163, December.

    More about this item

    Keywords

    bank specific factor; macroeconomic factor; credit risk;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:90566. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.