IDEAS home Printed from https://ideas.repec.org/a/pts/journl/y2017i2p41-47.html
   My bibliography  Save this article

Logistic Regression As A Tool For Determination Of The Probability Of Default For Enterprises

Author

Listed:
  • Erika SPUCHLAKOVA

    (University of Zilina, Faculty of Operation and Economics of Transport and Communications, Slovak Republic)

  • Maria KOVACOVA

    (University of Zilina, Faculty of Operation and Economics of Transport and Communications, Slovak Republic)

Abstract

In a rapidly changing world it is necessary to adapt to new conditions. From a day to day approaches can vary. For the proper management of the company it is essential to know the financial situation. Assessment of the company financial health can be carried out by financial analysis which provides a number of methods how to evaluate the company financial health. Analysis indicators are often included in the company assessment, in obtaining bank loans and other financial resources to ensure the functioning of the company. As company focuses on the future and its planning, it is essential to forecast the future financial situation. According to the results of company´s financial health prediction, the company decides on the extension or limitation of its business. It depends mainly on the capabilities of company´s management how they will use information obtained from financial analysis in practice. The findings of logistic regression methods were published firstly in the 60s, as an alternative to the least squares method. The essence of logistic regression is to determine the relationship between being explained (dependent) variable and explanatory (independent) variables. The basic principle of this static method is based on the regression analysis, but unlike linear regression, it can predict the probability of a phenomenon that has occurred or not. The aim of this paper is to determine the probability of bankruptcy enterprises.

Suggested Citation

  • Erika SPUCHLAKOVA & Maria KOVACOVA, 2017. "Logistic Regression As A Tool For Determination Of The Probability Of Default For Enterprises," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 16(2), pages 41-47.
    • Handle: RePEc:pts:journl:y:2017:i:2:p:41-47
    as

    Download full text from publisher

    File URL: http://economic.upit.ro/repec/pdf/2017_2_5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    2. Bianco, Ana M. & Martínez, Elena, 2009. "Robust testing in the logistic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4095-4105, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Bălăcescu Aniela & Zaharia Radu Șerban, 2019. "Aspects of the Evolution of the Romanian Tourists’ Preferences Concerning the Domestic Tourist Destinations," Valahian Journal of Economic Studies, Sciendo, vol. 10(1), pages 21-28, July.

    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. Marco Botta & Luca Colombo, 2016. "Macroeconomic and Institutional Determinants of Capital Structure Decisions," DISCE - Working Papers del Dipartimento di Economia e Finanza def038, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    2. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    3. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    4. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    5. Richardson, Grant & Taylor, Grantley & Lanis, Roman, 2015. "The impact of financial distress on corporate tax avoidance spanning the global financial crisis: Evidence from Australia," Economic Modelling, Elsevier, vol. 44(C), pages 44-53.
    6. Kristóf, Tamás, 2008. "A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről [Some methodological questions of bankruptcy prediction and probability of default estimation]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 441-461.
    7. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    8. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    9. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.
    10. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    11. Maurice Peat, 2007. "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 303-324, September.
    12. Wen Su, 2021. "Default Distances Based on the CEV-KMV Model," Papers 2107.10226, arXiv.org, revised May 2022.
    13. Amir Ghafourian Shagerdi & Ali Mahdavipour & Reza Jahanshiri Ariyan Tashakori Baghdar & Mohammad Sajjad Ghafourian Shagerdi, 2020. "Investment Efficiency and Audit Fee from the Perspective of the Role of Financial Distress," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 318-333.
    14. Meles, Antonio & Salerno, Dario & Sampagnaro, Gabriele & Verdoliva, Vincenzo & Zhang, Jianing, 2023. "The influence of green innovation on default risk: Evidence from Europe," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 692-710.
    15. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    16. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    17. Jing Zeng & Xiongyuan Wang & Kam C. Chan, 2021. "Does the value‐added tax Reform increase a firm’s collateral bank loans? Evidence from China," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 29(4), pages 681-710, October.
    18. Jason J. Constable & David R. Woodliff, 1994. "Predicting Corporate Failure Using Publicly Available Information," Australian Accounting Review, CPA Australia, vol. 4(7), pages 13-27, May.
    19. Guido Max Mantovani & Gregory Gadzinski, 2022. "How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts," JRFM, MDPI, vol. 15(11), pages 1-18, October.
    20. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.

    More about this item

    Keywords

    Enterprise; Logistic regression; Probability of default.;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    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:pts:journl:y:2017:i:2:p:41-47. 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: Alina Hagiu (email available below). General contact details of provider: https://edirc.repec.org/data/fepitro.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.