BUSINESS INFORM JOURNAL.

УКР

ENG

Search:

REGISTRATION CERTIFICATE

KV #19905-9705 PR dated 02.04.2013.

FOUNDERS

RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE)

According to the decision No. 802 of the National Council of Television and Radio Broadcasting of Ukraine dated 14.03.2024, is registered as a subject in the field of print media.
ID R30-03156

PUBLISHER

Liburkina L. M.

SITE SECTIONS

Selection of Econometric Instruments when Building a Scoring Model Based on Dummy Variables
Savina S. S., Vodzyanova N. K., Bilyk T. O., Kravchenko V. L., Semashko K. A.

Savina, Svitlana S. et al. (2023) “Selection of Econometric Instruments when Building a Scoring Model Based on Dummy Variables.” Business Inform 6:128–133.
https://doi.org/10.32983/2222-4459-2023-6-128-133

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
Downloads/views: 0

Download article (pdf) -

UDC 519.866

Abstract:
The aim of the study is to select adequate econometric instruments for building a scoring model on a specific array of initial data, which contains the vast majority of fictitious variables. Despite a significant number of developments devoted to the construction of scoring models, a universal method allowing to obtain a highly efficient classifier for any data has not been identified. Therefore, the task of selection of the best method for building a scoring model remains relevant, depending on the characteristics of the available data. The most successful approach when selecting a model for solving the problem of binary classification is the use of several types of econometric models and the choice of the best of them according to the results of classification. In the presented study, the following types of models were applied: discriminant model, logit and probit regressions, and polynomial logistic regression. Training samples with different structure were used. Comparison of all obtained models allows us to conclude that polynomial logistic regression is preferable in this case. This model demonstrates high classification rates for all introduced object classes and has an important advantage compared to models that make a binary selection. The advantage of polynomial logistic regression is also the possibility of selecting in each case a convenient scale for dividing borrowers into more than two classes and determining the level of probability of reliability of the borrower acceptable for its own conditions, at which it should be assigned to one of the selected classes. Prospects for further research in this direction are the use of machine learning methods that will be able to use ensembles of the best of the considered models. In addition, the proposed models can be used in solving similar problems in other spheres of economic activity.

Keywords: scoring model, logistic regression, polynomial logistic regression, binary classification.

Tabl.: 5. Formulae: 1. Bibl.: 10.

Savina Svitlana S. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]
Vodzyanova Natalia K. – Senior Lecturer, Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]
Bilyk Tetiana O. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]
Kravchenko Viktoriia L. – Senior Lecturer, Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]
Semashko Kateryna A. – Candidate of Sciences (Economics), Senior Lecturer, Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]

List of references in article

Kuznietsova, N. V. “Rozrobka skorynhovykh kart dlia analizu ryzykiv bankivskoi diialnosti“ [Development of Scoring Cards for Analyzing the Risks of Banking Activity]. Reiestratsiia, zberihannia i obrobka danykh, vol. 19, no. 4 (2017): 35-44. DOI: https://doi.org/10.35681/1560-9189.2017.19.4.142920
Sydorenko, N. “A Beginner's Guide To B2B Lead Scoring“. https://snov.io/blog/guide-to-lead-scoring/
Siddiqi, N. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons, 2005.
Bastos, J. A. “Predicting Credit Scores with Boosted Decision Trees“. Forecasting, vol. 4, no. 4 (2022): 925-935. DOI: https://doi.org/10.3390/forecast4040050
Vasyliev, O. M. “Skorynhove modeliuvannia na osnovi neironnykh merezh dlia vyznachennia reitynhu pozychalnyka banku“ [Scoring Modeling Based on Neural Networks for Determining a Bank Borrower's Rating]. Ekonomika Ukrainy, no. 10 (2020): 54-62. DOI: https://doi.org/10.15407/economyukr.2020.10.054
Garcin, M., and Stephan, S. “Credit scoring using neural networks and SURE posterior probability calibration“. 2021. https://hal.science/hal-03286760v1/file/Article_VF.pdf
Savina, S. S., and Vodzianova, N. K. “Pobudova skorynhovoi modeli dlia masyvu danykh na osnovi fiktyvnykh zminnykh“ [Building a Scoring Model for a Data Array Based on Dummy Variables]. Technologies and strategies for the implementation of scientific achievements : collection of scientific papers «SCIENTIA» with Proceedings of the III International Scientific and Theoretical Conference. April 28, 2023. Stockholm, Kingdom of Sweden: European Scientific Platform. 58-59. DOI: https://doi.org/10.36074/scientia-28.04.2023
Savina, S. S., and Vodzianova, N. K. “Pobudova modeli otsinky imovirnosti defoltu pozychalnykiv na osnovi yakisnykh pokaznykiv“ [Building a Model for Estimating the Probability of Borrower Default Based on Qualitative Indicators]. Rozvytok naukovoi dumky postindustrialnoho suspilstva: suchasnyi dyskurs. 2023. https://archive.mcnd.org.ua/index.php/conference-proceeding/issue/view/28.04.2023/25
Savina, S. S., and Vodzianova, N. K. “Zastosuvannia polinomialnoi lohistychnoi rehresii pry pobudovi skorynhovykh modelei“ [Application of Polynomial Logistic Regression in Building Scoring Models]. Aktualni pytannia rozvytku haluzei nauky. Vinnytsia: Yevropeiska naukova platforma, 2023. 130-132. DOI 10.36074/mcnd-12.05.2023
Brooks, Ch. Introductory Econometrics for Finance. Cambridge University Press, 2014.

FOR AUTHORS

License Contract

Conditions of Publication

Article Requirements

Regulations on Peer-Reviewing

Publication Contract

Current Issue

Frequently asked questions

INFORMATION

The Plan of Scientific Conferences

OUR PARTNERS

Journal «The Problems of Economy»

The site and its metadata are licensed under CC BY-SA.

Write to webmaster