IDEAS home Printed from https://ideas.repec.org/a/rau/journl/v10y2015i3p57-82.html
   My bibliography  Save this article

Econometric Model Used In Decision-Making Process Of Company Financing

Author

Listed:
  • Florin Mihai Magda

    (Babes-Bolyai University)

  • Adina Elena Danuletiu

    (1 Decembrie 1918 University)

Abstract

Over time, SMEs financing resulted in many debates starting with the general economic background up to the correct interpretation of financial statements on which the financing decision is based. In a more restricted way, the financing decision is finding, in the most appropriate and fair manner, the correlations between financial data, and it concerns all Romania-based financial institutions,mindful of the lessons taught by the financial crisis, the new Basel III. Thus, banks have created internal rating models, proving the viability of the loan applicant by calculating the PD. In this case study, we conducted an econometric modeling based on the logistic regression which, starting from a financial module consisting of 7 ratios, calculates the PD for a time horizon of 12 months, for a company applying for funding. The study highlights, both theoretically and practically, the advantages and the limits of econometric modeling. We used data from 25 Romanian-based companies, collected over a period of 4 years, from 2008 to 2011, resulting in a database consisting of 100 observations processed using the STATA statistical processing software. The results of the econometric model – the impact over the PD are interpreted and validated by calculating the odds ratio of PD.

Suggested Citation

  • Florin Mihai Magda & Adina Elena Danuletiu, 2015. "Econometric Model Used In Decision-Making Process Of Company Financing," Romanian Economic Business Review, Romanian-American University, vol. 10(3), pages 57-82, September.
  • Handle: RePEc:rau:journl:v:10:y:2015:i:3:p:57-82
    as

    Download full text from publisher

    File URL: http://www.rebe.rau.ro/RePEc/rau/journl/FA15/REBE-FA15-A5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    2. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    3. Altman, Edward I., 2005. "An emerging market credit scoring system for corporate bonds," Emerging Markets Review, Elsevier, vol. 6(4), pages 311-323, December.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Micha, Bernard, 1984. "Analysis of business failures in France," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 281-291, June.
    6. Gentry, Ja & Newbold, P & Whitford, Dt, 1985. "Classifying Bankrupt Firms With Funds Flow Components," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 146-160.
    7. Mossman, Charles E, et al, 1998. "An Empirical Comparison of Bankruptcy Models," The Financial Review, Eastern Finance Association, vol. 33(2), pages 35-53, 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. 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.
    2. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    3. Bastien Lextrait, 2021. "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers 2021-25, University of Paris Nanterre, EconomiX.
    4. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    5. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    6. Philosophov, Leonid V. & Philosophov, Vladimir L., 2002. "Corporate bankruptcy prognosis: An attempt at a combined prediction of the bankruptcy event and time interval of its occurrence," International Review of Financial Analysis, Elsevier, vol. 11(3), pages 375-406.
    7. Onofrei, Mihaela & Lupu, Dan, 2014. "The modelling of forecasting the bankruptcy in Romania," MPRA Paper 95511, University Library of Munich, Germany.
    8. repec:ath:journl:tome:34:v:2:y:2014:i:34:p:99-109 is not listed on IDEAS
    9. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    10. Antonio David Somoza Lopez & Josep Vallverdu Calafell, 2003. "Una comparacion de la seleccion de los ratios contables en los modelos contable-financieros de prediccion de la insolvencia empresarial," Working Papers in Economics 94, Universitat de Barcelona. Espai de Recerca en Economia.
    11. Ha Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," Working Papers hal-04141336, HAL.
    12. Laitinen, Erkki K., 2007. "Classification accuracy and correlation: LDA in failure prediction," European Journal of Operational Research, Elsevier, vol. 183(1), pages 210-225, November.
    13. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    14. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.
    15. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    16. Sebastian Klaudiusz Tomczak & Edward Radosiński, 2017. "The effectiveness of discriminant models based on the example of the manufacturing sector," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(3), pages 81-97.
    17. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    18. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    19. Nicoleta BARBUTA-MISU, 2011. "A Specific Model for Assessing the Financial Performance:Case study on Building Sector Enterprises of Galati County - Romania," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 318-325.
    20. Tarek Ibrahim Eldomiaty & Mohamed Hashem Rashwan & Mohamed Bahaa El Din & Waleed Tayel, 2016. "Firm, industry and economic determinants of working capital at risk," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 1-29, December.
    21. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classification," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 133-150, June.

    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:rau:journl:v:10:y:2015:i:3:p:57-82. 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: Alex Tabusca (email available below). General contact details of provider: https://edirc.repec.org/data/ferauro.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.