BUSINESS INFORM JOURNAL.

УКР

ENG

Search:

REGISTRATION CERTIFICATE

KV #19905-9705 PR dated 02.04.2013.

FOUNDERS

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

ROR

EDRPOU 05481984

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.

CATALOG

The Original Industry Discriminant Model for Assessing the Financial and Economic Condition of Domestic Companies For Electric Power Distribution
Kutsenko V. O., Ihnashkina T. B.

Kutsenko, Vladyslav O., and Ihnashkina, Tetiana B. (2026) “The Original Industry Discriminant Model for Assessing the Financial and Economic Condition of Domestic Companies For Electric Power Distribution.” Business Inform 8:107–119.
https://doi.org/10.32983/2222-4459-2026-8-107-119

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
Downloads/views: 0

Download article (pdf) -

UDC 336.64:[658.15:303.725.3]:621.311.1

Abstract:
Ukrainian science and practice need to expand their arsenal of domestic discriminant models with new developments. The authors’ goal is to build their own industry-specific model for assessing the financial and economic condition of companies in the electric power distribution system, based on the theory of discriminant analysis. The aim of this article is to present the results of the modeling in a systematic way. The work uses the following methods: abstract-logical – to select the dependent non-metric variable for dividing objects into mutually exclusive groups; comparative – to compare and analyze a large number of financial indicators in order to form an initial database that will be used in the process of multivariate modeling; ROC analysis – as an alternative way to determine the independent variables that best discriminate the groups; discriminant analysis – for building its own discriminant model, assessing its statistical significance and validity. As an information base for the study, educational and scientific publications on multivariate analysis were used, as well as the official financial statements of electric power distribution operators (regional energy companies); as software – the IBM SPSS Statistics package. When developing the model, considering the impossibility of separating two mutually exclusive groups by the «bankrupt / not bankrupt» principle (as done in famous Z-models for predicting bankruptcy probability), the following main conceptual decisions were made: a) the model was named the «D-model for assessing financial and economic condition»; b) two mutually exclusive groups were distinguished according to the criterion of «stable / unstable» financial and economic condition; c) the distribution of regional energy companies according to this criterion was carried out taking into account the current military-political realities in the country based on the regional principle, namely: group 1 includes regional energy companies (10 legal entities) that are located in combat zones or close to them; group 2 includes those that are located farther from such zones (13 legal entities); g) due to the small size of the studied sample (23 regional energy companies), a limited number of independent variables – 9 indicators – was used (one less than the size of the smaller group 1). The final discriminant model contains 5 independent variables: coverage ratio (X1); debt ratio (X4); accounts receivable turnover ratio (X5); accounts payable turnover ratio (X6); net profit margin (X8). The determined statistical characteristics of the modeling results and the conducted verification procedures indicate high quality, reliability, and accuracy of the constructed model; in particular, Wilks’ lambda is 0.090; the canonical correlation coefficient is 0.954; the coefficient of determination is 0.910; Pearson’s criterion is 44.457; significance level <0.001; and the classification accuracy of enterprises is 100%. The next stage of the research is planned to involve practical testing of the constructed model.

Keywords: discriminant analysis; conceptual principles; improvement; new discriminant D-model; financial and economic condition; regional energy company; descriptive statistics; verification procedures.

Fig.: 2. Tabl.: 7. Formulae: 4. Bibl.: 19.

Kutsenko Vladyslav O. – Postgraduate Student, Department of Economics and Entrepreneurship named after T. H. Benia, Ukrainian State University of Science and Technologies (2 Akademika Lazariana Str., Dnipro, 49010, Ukraine)
Email: [email protected]
Ihnashkina Tetiana B. – Candidate of Sciences (Economics), Associate Professor, Professor, Department of Economics and Entrepreneurship named after T. H. Benia, Ukrainian State University of Science and Technologies (2 Akademika Lazariana Str., Dnipro, 49010, Ukraine)
Email: [email protected]

List of references in article

Aktsionerne tovarystvo «DTEK Dniprovski elektromerezhi». DTEK Dniprovski elektromerezhi [DTEK Dnieper Grids]. https://uk.wikipedia.org/wiki/DTEKDniprovskielektromerezhi
Fawcett T. (2006). An Introduction to ROC Analysis. Pattern Recognition Letters, 8(27), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Field A. (2013). Discovering Statistics Using IBM SPSS Statistics. 4th ed. London: Sage Publications. https://ru.scribd.com/document/1047536901/Discovering-Statistics-using-IBM-SPSS-Statistics-4th-Edition-Field-Andy-Ebook
Hair J. F. Jr., Black W. C., Babin B. J. & Anderson R. E. (2019). Multivariate Data Analysis. 8th ed. Andover: Cengage Learning. https://www.cengage.uk/c/multivariate-data-analysis-8e-hair-babin-anderson-black/9781473756540/
Hanley J. A. & McNeil B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1(143), 29–36. https://doi.org/10.1148/radiology.143.1.7063747
Huberty C. J. & Olejnik S. (2006). Applied MANOVA and Discriminant Analysis. Hoboken, NJ: John Wiley & Sons. https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780471789475.fmatter
Ihnashkina T. B. & Kutsenko V. O. (2025). Analiz suchasnoho stanu vitchyznianoi nauky z pytan vyznachennia ymovirnosti bankrutstva pidpryiemstv [Analysis of the current state of domestic science on determining the probability of enterprise bankruptcy]. Biznes Inform, 11, 289–298. https://doi.org/10.32983/2222-4459-2025-11-289-298
Ihnashkina T. B. & Kutsenko V. O. (2026). Zarubizhni ta vitchyzniani Z-modeli ymovirnosti bankrutstva: pohlyblenyi analiz vytokiv yikh rozrobky [Foreign and domestic Z-models of bankruptcy probability: an in-depth analysis of the origins of their development]. Ekonomichnyi prostir, 210, 238–246. https://doi.org/10.30838/EP.210.238-246
Johnson R. A. & Wichern D. W. (2007). Applied Multivariate Statistical Analysis. 6th ed. Upper Saddle River, NJ: Pearson Prentice Hall. https://pdfcoffee.com/applied-multivariate-statistical-analysis-6th-edition-pdf-free.html
Klebanova T. S., Hurianova L. S., Chahovets L. O., Panasenko O. V., Serhiienko O. A. & Yatsenko R. M. (2024). Biznes analityka bahatovymirnykh protsesiv [Business analytics of multidimensional processes]. Kharkiv: KhNEU im. S. Kuznetsia. http://ebooks.git-elt.hneu.edu.ua/babap/
McLachlan G. J. (2004). Discriminant Analysis and Statistical Pattern Recognition. New York: John Wiley & Sons. https://www.wiley.com/en-us/shop/general-introductory-statistics/discriminant-analysis-and-statistical-pattern-recognition-p-9780471691150
Metz C. E. (1978). Basic Principles of ROC Analysis. Seminars in Nuclear Medicine, 4(8), 283–298. https://doi.org/10.1016/s0001-2998(78)80014-2
Morrison D. F. (2005). Multivariate Statistical Methods. 4th ed. Belmont, CA: Thomson Brooks/Cole. https://dokumen.pub/multivariate-statistical-methods-4nbsped-0534387780.html
Ponomarenko V. S. & Maliarets L. M. (2009). Bahatovymirnyi analiz sotsialno ekonomichnykh system [Multidimensional analysis of socio-economic systems]. Kharkiv: Vyd. KhNEU. https://repository.hneu.edu.ua/jspui/handle/123456789/3113
PrAT «Pidpryiemstvo z ekspluatatsii elektrychnykh merezh “Tsentralna Enerhetychna Kompaniia”». Pro tovarystvo [About the company]. https://cek.dp.ua/index.php/tovarystvo/pro-tovarystvo.html
Rencher A. C. (2002). Methods of Multivariate Analysis. 2nd ed. New York: John Wiley & Sons. https://www.wiley.com/en-us/shop/general-introductory-statistics/methods-of-multivariate-analysis-2nd-edition-p-9780471461722
Stevens J. P. (2009). Applied Multivariate Statistics for the Social Sciences. 5th ed. New York: Routledge. https://www.amazon.com/Applied-Multivariate-Statistics-Social-Sciences/dp/0805859039
Timm N. H. (2002). Applied Multivariate Analysis. New York: Springer. https://www.amazon.com/Applied-Multivariate-Statistics-Social-Sciences/dp/0805859039
Yarovyi A. T. & Strakhov Ye. M. (2015). Bahatovymirnyi statystychnyi analiz [Multivariate statistical analysis]. Odesa: Astroprynt.