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Applicability of bankruptcy probability assessment models to financial sector companies

Abstract

Anticipating the likelihood of bankruptcy is essential for every business. Nevertheless, one of the most critical sectors of the economy is the financial sector. The bankruptcy of a company in the financial industry affects both individuals, businesses, and organizations, negatively impacting the economy. Therefore, it is essential to anticipate the likelihood of bankruptcy of companies in the financial sector and make management decisions to avoid the risk of bankruptcy. There are many methodologies for analyzing and predicting corporate bankruptcy that differ in content, number, and accuracy of measurable indicators, so it is crucial to identify which model is most appropriate for assessing the risk of corporate bankruptcy in a particular sector. Given that the financial sector plays a crucial role in economic development and that the consequences of their bankruptcy are harrowing, the aim is to identify the most sensitive model to the risk of bankruptcy in this sector. To achieve this goal, we reveal the concept of bankruptcy, present the internal and external causes of bankruptcy, and systematize the results of bankruptcy risk research of companies in the financial sector. Bankruptcy probability assessment models are also presented and compared, and their applicability to the financial industry is discussed. An analysis of the literature revealed that the most commonly used models for assessing the bankruptcy risk of companies in the financial sector are the Altman Z Index, the Ohlson O Index, and the Zmijewski X Index. After applying these models in the case of three banks (SEB bank, UAB Medicinos bank, and General Financing bank) using 2021. The Altman Z model found that the most sensitive bankruptcy predictor was the probability of bankruptcy.


Article in Lithuanian.


Bankroto tikimybės vertinimo modelių pritaikomumas finansų sektoriaus įmonėms


Santrauka


Iš anksto numatyti bankroto tikimybę yra svarbu kiekvienai įmonei. Vis dėlto vienas svarbiausių ekonomikos sektorių yra finansų sektorius. Finansų sektoriaus įmonės bankrotas paliečia tiek privačius asmenis, tiek verslo įmones, organizacijas ir lemia neigiamas pasekmes visai ekonomikai. Todėl labai svarbu iš anksto numatyti finansinio sektoriaus įmonių bankroto tikimybę ir priimti valdymo sprendimus siekiant išvengti bankroto rizikos. Yra daug metodikų, kaip analizuoti ir numatyti įmonės bankrotą, jos skiriasi turiniu, vertinamų rodiklių skaičiumi ir tikslumu, todėl svarbu identifikuoti, koks modelis yra tinkamiausias konkretaus sektoriaus įmonių bankroto rizikai vertinti. Atsižvelgiant į tai, kad finansų sektorius atlieka lemiamą vaidmenį ekonominio vystymosi procese ir jų bankroto pasekmės labai skaudžios, keliamas tikslas identifikuoti modelį, jautriausiai prognozuojantį bankroto riziką šiame sektoriuje. Įgyvendinant šį tikslą, straipsnyje yra atskleidžiama bankroto samprata, pateikiamos vidinės ir išorinės bankroto priežastys, susisteminti finansų sektoriaus įmonių bankroto rizikos tyrimų rezutatai. Taip pat pateikiami ir palyginami bankroto tikimybės vertinimo modeliai, aptariamas jų pritaikomumas finansų sektoriui. Literatūros analizė atskleidė, kad finansų sektoriaus įmonių bankroto rizikai vertinti dažniausiai taikomi modeliai yra Altman’o Z indeksas, Ohlson’o O indeksas, Zmijewski X indeksas. Pritaikius šiuos modelius trijų bankų (SEB banko, UAB Medicinos banko bei General Financing banko) atveju, naudojant 2021 m. finansines ataskaitas, nustatyta, kad jautriausiai bankroto tikimybę prognozuoja Altman’o Z modelis.


Reikšminiai žodžiai: bankrotas, bankroto tikimybė, bankroto tikimybės vertinimo modeliai, finansinis sektorius.

Keyword : bankruptcy, probability of bankruptcy, bankruptcy probability assessment models, financial sector

How to Cite
Grikietytė, G., & Mačiulytė-Šniukienė, A. (2023). Applicability of bankruptcy probability assessment models to financial sector companies. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 15. https://doi.org/10.3846/mla.2023.17761
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Jan 20, 2023
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