PREDICTION OF INSOLVENCY USING LOGISTIC REGRESSION: THE CASE OF THE REPUBLIC OF SRPSKA

被引:0
|
作者
Mujkic, Elvis [1 ]
Poljasevic, Jelena [2 ]
机构
[1] Molson Coors BH, Banja Luka 78000, Bosnia & Herceg
[2] Univ Banja Luka, Fac Econ, Banja Luka 78000, Bosnia & Herceg
来源
EKONOMSKI VJESNIK | 2023年 / 36卷 / 01期
关键词
Insolvency; bankruptcy; financial indicators; logistic regression; Republic of Srpska; trade; SUCCESS;
D O I
10.51680/ev.36.1.10
中图分类号
F [经济];
学科分类号
02 ;
摘要
Purpose: In this paper, the authors try to develop a model for predicting the insolvency of trading compa-nies from the Republic of Srpska. The research seeks to determine the statistically most significant financial indicator in predicting the insolvency of trading companies in the Republic of Srpska.Methodology: The research data sample in this paper consists of yearly data from 2017 to 2020 for two hundred trading companies from the Republic of Srpska. Binary logistic regression was used to develop the model.Results: As a result of the research, a model was created that successfully classifies 82.9% of solvent and 80% of insolvent companies, with a general efficiency rate of 81.4%.Conclusion: Based on the empirical research results, we can conclude that the hypothesis has been con-firmed that the LR model can be formed on the basis of selected financial indicators as a tool for predicting the insolvency of trading companies in the Republic of Srpska.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Prediction of Deterioration Level of Heritage Buildings Using a Logistic Regression Model
    Chen, Si
    Chen, Jingjing
    Yu, Jiming
    Wang, Tao
    Xu, Jian
    [J]. BUILDINGS, 2023, 13 (04)
  • [32] Prediction of mortality of premature neonates using neural network and logistic regression
    Rezaeian, Aramesh
    Rezaeian, Marzieh
    Khatami, Seyede Fatemeh
    Khorashadizadeh, Fatemeh
    Moghaddam, Farshid Pouralizadeh
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2022, 13 (03) : 1269 - 1277
  • [33] A Study on the Prediction Model for International Trade Payment Using Logistic Regression
    Joo, Hye-Young
    Lee, Dong-Jun
    [J]. JOURNAL OF KOREA TRADE, 2021, 25 (02): : 111 - 133
  • [34] Driving risk status prediction using Bayesian networks and logistic regression
    Yan, Lixin
    Huang, Zhen
    Zhang, Yishi
    Zhang, Liyan
    Zhu, Dunyao
    Ran, Bin
    [J]. IET INTELLIGENT TRANSPORT SYSTEMS, 2017, 11 (07) : 431 - 439
  • [35] Cancer classification and prediction using logistic regression with Bayesian gene selection
    Zhou, XB
    Liu, KY
    Wong, STC
    [J]. JOURNAL OF BIOMEDICAL INFORMATICS, 2004, 37 (04) : 249 - 259
  • [36] Heart Disease Prediction Using Logistic Regression Machine Learning Model
    Hrvat, Faris
    Spahic, Lemana
    Aleta, Amina
    [J]. MEDICON 2023 AND CMBEBIH 2023, VOL 1, 2024, 93 : 654 - 662
  • [37] Rapid prediction of landslide dam stability using the logistic regression method
    Yibo Shan
    Shengshui Chen
    Qiming Zhong
    [J]. Landslides, 2020, 17 : 2931 - 2956
  • [38] Developing prediction models for clinical use using logistic regression: an overview
    Shipe, Maren E.
    Deppen, Stephen A.
    Farjah, Farhood
    Grogan, Eric L.
    [J]. JOURNAL OF THORACIC DISEASE, 2019, 11 : S574 - S584
  • [39] LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers
    Hou, Ting
    Zheng, Guangyong
    Zhang, Pingyu
    Jia, Jia
    Li, Jing
    Xie, Lu
    Wei, Chaochun
    Li, Yixue
    [J]. PLOS ONE, 2014, 9 (02):
  • [40] Prediction of mortality of premature neonates using neural network and logistic regression
    Aramesh Rezaeian
    Marzieh Rezaeian
    Seyede Fatemeh Khatami
    Fatemeh Khorashadizadeh
    Farshid Pouralizadeh Moghaddam
    [J]. Journal of Ambient Intelligence and Humanized Computing, 2022, 13 : 1269 - 1277