IDEAS home Printed from https://ideas.repec.org/a/ora/journl/v2y2023i2p67-75.html
   My bibliography  Save this article

Decision Tree Or Logistic Regression - Which Basic Model Is Better?

Author

Listed:
  • Kitti Fodor

    (Department of Business Statistics and Economic Forecasting, Faculty of Economics, University of Miskolc, Miskolc, Hungary)

Abstract

In this paper, my aim is to show which of the data in the Central Credit Information System are the ones that influence the factors that are then used to perform the analysis using a decision tree and logistic regression, and I would like to know, which of the two basic model is the better one. For the analyses, I used a random sample of 500 items, reflecting the proportions of performing and nonperforming loans in the population. For both methods, one variable was found to be significant, which was the ratio of the repayment to the contract amount, so this is the most significant of the data recorded by the Central Credit Information System in terms of loan defaults. If I compare the two methods, I can conclude that both methods have a high level of accuracy, but logistic regression is the one that produced better results, as it was able to identify a higher proportion of defaulted loans. Unfortunately, the decision tree could not identify any defaulting loans despite its higher classification accuracy. The reason can be the unfavourable sample composition. Finally, the logistic regression was able to categorize the transactions with 81,1% accuracy and has better AUC value and better value for Gini coefficients.

Suggested Citation

  • Kitti Fodor, 2023. "Decision Tree Or Logistic Regression - Which Basic Model Is Better?," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 32(2), pages 67-75, December.
    • Handle: RePEc:ora:journl:v:2:y:2023:i:2:p:67-75
    as

    Download full text from publisher

    File URL: https://anale.steconomiceuoradea.ro/en/wp-content/uploads/2024/03/Volume-2_AUOES_december-2023-70-78.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    3. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    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. 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.
    2. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    3. Catherine Refait, 2004. "La prévision de la faillite fondée sur l’analyse financière de l’entreprise : un état des lieux," Économie et Prévision, Programme National Persée, vol. 162(1), pages 129-147.
    4. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," 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. 26(1), pages 294-314, Diciembre.
    5. Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.
    6. Foo See Liang & Shaak Pathak, 2019. "Understanding the Connection of Performance and Z-Scores for Manufacturing Firms in South Korea," Journal of Asian Development, Macrothink Institute, vol. 5(3), pages 37-46, November.
    7. repec:zbw:bofrdp:2009_035 is not listed on IDEAS
    8. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    9. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    10. M. A. Lagesh & Maram Srikanth & Debashis Acharya, 2018. "Corporate Performance during Business Cycles: Evidence from Indian Manufacturing Firms," Global Business Review, International Management Institute, vol. 19(5), pages 1261-1274, October.
    11. Esteban Alfaro Cortés & Matías Gámez Martínez & Noelia García Rubio, 2007. "Multiclass Corporate Failure Prediction by Adaboost.M1," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(3), pages 301-312, August.
    12. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Documents de travail du Centre d'Economie de la Sorbonne 16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    13. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, Sciendo, vol. 5(2), pages 23-45, September.
    14. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    15. Fernando García & Francisco Guijarro & Ismael Moya, 2013. "Monitoring credit risk in the social economy sector by means of a binary goal programming model," Service Business, Springer;Pan-Pacific Business Association, vol. 7(3), pages 483-495, September.
    16. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
    17. Sebastian Klaudiusz Tomczak & Anna Skowrońska-Szmer & Jan Jakub Szczygielski, 2020. "Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis," Energies, MDPI, vol. 13(2), pages 1-27, January.
    18. Harlan D. Platt & Marjorie B. Platt, 2008. "Financial Distress Comparison Across Three Global Regions," JRFM, MDPI, vol. 1(1), pages 1-34, December.
    19. Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
    20. Dawen Yan & Guotai Chi & Kin Keung Lai, 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models," Mathematics, MDPI, vol. 8(8), pages 1-27, August.
    21. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.

    More about this item

    Keywords

    loan default; decision tree; logistic regression; random sample; classification; ROC curve;
    All these keywords.

    JEL classification:

    • B16 - Schools of Economic Thought and Methodology - - History of Economic Thought through 1925 - - - Quantitative and Mathematical
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:ora:journl:v:2:y:2023:i:2:p:67-75. 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: Catalin ZMOLE The email address of this maintainer does not seem to be valid anymore. Please ask Catalin ZMOLE to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/feoraro.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.