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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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References

Ahmadpour Kasagari, A., Salehnezhad, S. H., & Ebadi, F. (2013). A review of bankruptcy and its prediction. International Journal of Academic Research in Accounting, Finance and Management Science, 3(4), 274–277.

Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinadef, O. O., & Bilalf, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164–184. https://doi.org/10.1016/j.eswa.2017.10.040

Al-Manaseer, S. R., & Al-Oshaibat, S. D. (2018). Validity of Altman Z-Score model to predict financial failure: Evidence from Jordan. International Journal of Economics and Finance, 10(8), 181–189. https://doi.org/10.5539/ijef.v10n8p181

Altman, E. I. (1968). The prediction of corporate bankruptcy: A discriminant analysis. The Journal of Finance, 23(1), 193–194. https://doi.org/10.1111/j.1540-6261.1968.tb03007.x

Anisom-Yaansah, F., Oware, K. M., & Samanhyia, S. (2016). Financial distress and bankruptcy prediction: Evidence from Ghana. Expert Journal of Finance, 4(1), 52–65.

Audito, apskaitos, turto vertinimo ir nemokumo valdymo tarnyba prie Lietuvos Respublikos finansų ministerijos. (2018). Bankroto priežastys. https://www.avnt.lt/assets/Apklausos/Bankroto-prieastys2018-12-31.pdf

Batchelor, T. (2018). Corporate bankruptcy: Testing the efficacy of the Altman Z-Score. International Research Journal of Applied Finance, 9(9), 404–414.

Beck, T., Demirguc-Kent, A., Levine, R., & Maksimovic, V. (2000). Financial structure and economic development: Firm, industry, and country evidence (Policy Research Working Paper Series 2423). The World Bank. https://doi.org/10.1596/1813-9450-2423

Boratyńska, K., & Grzegorzewska, E. (2018). Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches. Journal of Business Research, 89, 175–181. https://doi.org/10.1016/j.jbusres.2018.01.028

Denis, D. J. (2018). SPSS data analysis for univariate, bivariate, and multivariate statistics. John Wiley & Sons, Inc. https://doi.org/10.1002/9781119465775

Dwiarti, R., Hazmi, S., Santose, A., & Rahman, Z. (2021). Does bankruptcy matter in non-banking financial sector companies? Evidence from Indonesia. The Journal of Asian Finance, Economics and Business, 8(3), 441–449. https://doi.org/10.13106/jafeb.2021.vol8.no3.0441

Erdogan, B. E. (2008). Bankruptcy prediction of Turkish commercial banks using financial ratios. Applied Mathematical Science, 2(60), 2973–2982.

General Financing bankas. (n. d.). 2021 m. finansinės ataskaitos ir metinis pranešimas. Žiūrėta 2022 m. vasario 5 d. Prieiga per internetą https://www.gfbankas.lt/assets/Uploads/Finansines-ataskaitos/2021-m.-GFB-metinis-pranesimas-ir-finansines-ataskaitos.pdf

Ghosh, B. (2017). Bankruptcy modelling of Indian public sector banks: Evidence from neural trace. International Journal of Applied Behavioral Economics, 6(2), 52–65. https://doi.org/10.4018/IJABE.2017040104

Helwege, J. (2009). Financial firm bankruptcy and systemic risk. Smeal College of Business, Penn State University. https://doi.org/10.2139/ssrn.1315316

Hensher, D. A., Jones, S., & Greene, W. H. (2007). An error component logit analysis of corporate bankruptcy and insolvency risk in Australia. Abacus, 43, 241–264. https://doi.org/10.1111/j.1467-6281.2007.00228.x

Indriyanti, M. (2019). The accuracy of financial distress prediction models: Empirical study on the Word’s 25 Biggest Tech Companies in 2015–2016 Forbes’s version. KnE Social Sciences, 3(11), 442–450. https://doi.org/10.18502/kss.v3i11.4025

Investopedia. (n.d.). Financial services sector. https://www.investopedia.com/ask/answers/030315/what-financial-services-sector.asp

Kazakevičiūtė, G. ir Budrionytė, R. (2019). Bankroto prognozavimo modeliai Europos bankų sektoriui. Buhalterinės apskaitos teorija ir praktika, 19, 3. https://doi.org/10.15388/batp.2019.3

Khaddafi, M., Falahuddin, Heikal, M., & Nandari, A. (2017). Analysis Z-score to predict bankruptcy in banks listed in Indonesia stock exchange. International Journal of Economics and Financial Issues, 7(3), 326–330.

Kiseleva, I. A., Kuznetsov, V. I., Sadovnikova, N. A., Pikalov, A. V., & Dolgaya, A. A. (2020). Models for assessing the probability bankruptcy of enterprises. Journal of Critical Reviews, 7(9), 1037–1042. https://doi.org/10.31838/jcr.07.09.191

Lietuvos Respublikos Seimas. (2001). Lietuvos Respublikos įmonių bankroto įstatymas (Nr. IX-216). https://e-seimas.lrs.lt/portal/legalAct/lt/TAD/TAIS.129687?jfwid=

Lietuvos Respublikos Seimas. (2019). Lietuvos Respublikos juridinių asmenų nemokumo įstatymas (Nr. XIII-2221). https://e-seimas.lrs.lt/portal/legalAct/lt/TAD/56df69a293fa11e9aab6d8dd69c6da66

Maccarthy, J. (2017). Using Altman Z-score and Beneish M-score models to detect financial fraud and corporate failure: A case study of Enron Corporation. International Journal of Finance and Accounting, 6(6), 159–166.

Medicinos bankas. (n. d.). 2021 metų finansinės ataskaitos. https://www.medbank.lt/lt/apie-banka/finansines-ataskaitos

Ohlson, J. A. (1980). Financial rations and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395

Prabowo, S. C. B. (2019). Analysis on the prediction of bankruptcy of cigarette companies listed in the Indonesia stoch exchange using Altman (Z-Score) model and Zmijewski (X-Score) model. Journal of Applied Management (JAM), 17(2), 254–260. https://doi.org/10.21776/ub.jam.2019.017.02.08

Ramdani, E. (2020). Financial distress analysis using the Zmijewski method. Journal Ilman Manajemen Fakultas Ekonomi, 6(1), 69–78. https://doi.org/10.34203/jimfe.v6i1.2032

SEB bankas. (n. d.). 2021 m. konsoliduotos finansinės ataskaitos. https://www.seb.lt/apie-seb/investuotojams#item-1

Serrano-Cinca, C., Fuertes-Callen, G., Butierrez-Nieto, B., & Cuellar-Fernandez, B. (2011). Path modeling to bankruptcy: Causes and symptoms of the banking crisis (CEB Working Paper no. 11/007). Brussels, Belgium.

Shi, Y., & Li, X. (2019). An overview of bankruptcy prediction models for corporate firms: A systematic literature review. Intangible Capital, 15(2), 114–127. https://doi.org.10.3926/ic.1354

Sinarti, & Sembiring, T. M. (2015). Bankruptcy prediction analysis of manufacturing companies listed in Indonesia stock exchange. International Journal of Economics and Financial Issues, 5(1S), 354–359.

Sutra Tanjung, P. (2020). Comparative analysis of Altman Z-Score, Springate, Zmijewski and Ohlson models in predicting financial distress. EPRA International Journal of Multidisciplinary Research, 6(3), 126–137. https://doi.org/10.36713/epra4162

Ullah, H., Wang, Z., Abbas, M. G., Zhang, F., Shahzad, U., & Mahmood, M. R. (2021). Association of financial distress and pridicted bankruptcy: The case of Pakistani Banking sector. Journal of Asian Finance, Economics and Business, 8(1), 573–585. https://doi.org/10.13106/jafeb.2021.vol8.no1.573

Vainienė, R. (2005). Ekonomikos terminų žodynas. Tyto Alba.

Vita, M. (2019). Corporate financial distress and bankruptcy risk prediction: An empirical analysis in the Italian Financial sector [Thesis in Financial Reporting & Performance Measurement]. Luiss Guido Carli Libera Universita Internazionale Degli Studi Sociali, Roma, Italy.

Vitonytė, D. ir Mačiulytė-Šniukienė, A. (2019). Bankroto tikimybės vertinimo modelis skirtingo mokumo ir pelningumo įmonėms. Iš 22-oji Lietuvos jaunųjų mokslininkų konferencija „Mokslas – Lietuvos ateitis“. Ekonomika ir vadyba (p. 1–9). Vilnius.

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859