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Title:Primerjava uspešnosti napovedi stečaja podjetja tradicionalnega Z-modela z novejšim Ycz-modelom
Authors:ID Černesl, Vida (Author)
ID Kolar, Iztok (Mentor) More about this mentor... New window
Files:.pdf UN_Cernesl_Vida_2023.pdf (1009,28 KB)
MD5: 096525A2D7FBCFCDDD0CE8B7D7537725
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:EPF - Faculty of Business and Economics
Abstract:V diplomskem delu obravnavamo področje napovedi stečaja podjetja, pri čemer tudi primerjamo uspešnost tradicionalnega Zmijewskijevega Z-modela z novejšim Ycz-modelom. Napoved stečaja je zelo koristna za mnoge deležnike, kot so upniki, investitorji, poslovni partnerji, dobavitelji in kupci. Da bi videli, kako zdravo je poslovanje podjetja, se ves čas razvijajo napovedni modeli. Ti modeli podjetjem omogočajo, da bolje zaščitijo svoje poslovanje, sprejmejo določene ukrepe in celo preprečijo stečaj. Modele oziroma metode na splošno delimo v dve skupini, in sicer ločimo statistične metode in metode strojnega učenja. Izbrana modela temeljita na logitni regresiji in multivariatni diskriminantni analizi, ki obe spadata pod statistične metode. V delu ugotavljamo, kako uspešna sta izbrana modela za napoved stečaja slovenskih podjetij eno, dve in tri leta pred začetkom stečajnega postopka. Analiza je izvedena na treh poljubno izbranih propadlih slovenskih podjetjih, ki so šla v stečaj v zadnjih petih letih. Na podlagi izvedene analize ugotavljamo, da je Ycz-model uspešnejši, saj večkrat pravilno in ustrezno napove stečaj.
Keywords:stečaj podjetja, modeli napovedi stečaja podjetja, Zmijewskijev model, Ycz-model, propadla slovenska podjetja
Place of publishing:Ptuj
Publisher:V. Černesl
Year of publishing:2023
PID:20.500.12556/DKUM-84824 New window
UDC:005.521
COBISS.SI-ID:164426243 New window
Publication date in DKUM:14.09.2023
Views:289
Downloads:38
Metadata:XML DC-XML DC-RDF
Categories:EPF
:
ČERNESL, Vida, 2023, Primerjava uspešnosti napovedi stečaja podjetja tradicionalnega Z-modela z novejšim Ycz-modelom [online]. Bachelor’s thesis. Ptuj : V. Černesl. [Accessed 23 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=84824
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:28.07.2023

Secondary language

Language:English
Title:Comparing the performance of traditional Z- and newer Ycz- bankruptcy prediction models
Abstract:In the bachelor’s thesis, we discuss the field of company bankruptcy forecasting while also comparing the performance of the traditional Zmijewski Z-model with the newer Ycz model. A declaration of bankruptcy is very beneficial for many stakeholders, such as creditors, investors, business partners, suppliers, and customers. With the purpose to realize how healthy a company's business is, predictive models are constantly being developed. This allows companies to protect their operations better, take certain measures, and even prevent bankruptcy. We generally divide models or methods into two groups: statistical methods and machine learning methods. The selected models are based on logit regression and multivariate discriminant analysis both of which belong to statistical methods. In the thesis, we ascertain how successful the selected models are for predicting the bankruptcy of Slovenian companies one, two, and three years before the start of bankruptcy proceedings. The analysis was carried out on three arbitrarily selected failed Slovenian companies that went bankrupt in the last five years. Based on the performed analysis, we conclude that the Ycz model is more successful because it predicts bankruptcy correctly and appropriately several times.
Keywords:company bankruptcy, company bankruptcy prediction models, Zmijewski's model, Ycz-model, bankrupted Slovenian companies


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