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Journal articles on the topic 'Altman's Z-Score Model'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Prof., Rohini Sajjan. "PREDICTING BANKRUPTCY OF SELECTED FIRMS BY APPLYING ALTMAN'S Z-SCORE MODEL." International Journal of Research – Granthaalayah 4, no. 4 (2017): 152–58. https://doi.org/10.5281/zenodo.846680.

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Predication of Bankruptcy is critical task. Early stage of identification of likelihood of solvency may avoid evils in the near future & may shelter the firm from Bankruptcy situation. Bankruptcy of organizations can be predicated by using Altman’s Z-Score Model. This study tries to apply the model to understand the likelihood of Bankruptcy of selected firms for past 5 years from 2011 to 2015 which are listed in BSE & NSE. Companies are selected from manufacturing & non-manufacturing sector. The study reveals that none of the companies completely belongs to Safe Zone except for few

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Erizal, Erizal, Margaretha Michella Gunawan, and M. Nasyubun. "Analisis Prediksi Kebangkrutan Perusahaan Asuransi Kerugian Di Indonesia." Journal of Economic, Bussines and Accounting (COSTING) 7, no. 2 (2024): 3245–55. http://dx.doi.org/10.31539/costing.v7i2.8901.

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The application of the bankruptcy model using the Altman Z-score model is carried out to predict financial difficulties by using balance sheet analysis which can reflect the company's financial performance. Insurance companies are needed to minimize risks and uncertainties for futureprotection. Altman's Z-score model is analyzed through the calculation results if it is smaller than <1.1 it is concluded that the company is at high risk, if the calculation results are 1.1-2.6 it is concluded that the company is prone to bankruptcy, and if the calculation result is> 2.6 itcan be said that t

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Zamzami, Zamzami, Osrita Hapsara, and Yunan Surono. "Kinerja Perusahaan Berdasarkan Pertumbuhan Investasi dan Potensi Kebangkrutan Sub Sektor Perkebunan di Bursa Efek Indonesia Periode 2014 – 2017." J-MAS (Jurnal Manajemen dan Sains) 4, no. 1 (2019): 14. http://dx.doi.org/10.33087/jmas.v4i1.66.

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This research aims to prove the stock sub group of the plantation sector has a positive value of the company (investment growth) if measured using model Tobin's q, and potential bankruptcy if measured using models Altman's Z-score first, Altman's Z-score and the Z-Altman's revision of the score as well as modifications to get the stocks that had the best performance based on the model. The sample in this study using census methods, namely the entire company group issuers shares sub plantation sector during the four-year period, starting from the observation of the year 2014-2017 recorded as ma

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Anandasayanan.S and V.A Subramaniam. "PREDICTING BANKRUPTCY OF SELECTED MANUFACTURING COMPANIES LISTED IN COLOMBO STOCK EXCHANGE: APPLYING ALTMAN'S Z-SCORE." International Journal of Research -GRANTHAALAYAH 5, no. 2 (2017): 313–21. https://doi.org/10.5281/zenodo.376051.

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Bankruptcy is the legal status for an individual or company incapable to pay off outstanding debt. Predication of Bankruptcy is critical task. Early stage of identification of likelihood of solvency may avoid evils in the near future & may shelter the firm from Bankruptcy situation. Bankruptcy of organizations can be predicated by using Altman’s Z-Score Model. This study tries to apply the model to understand the likelihood of Bankruptcy of selected listed manufacturing firms for past 5 years from 2010 to 2014 which are listed in Colombo Stock Exchange. The study reveals that four companie

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Aradhana Sorout and Netra Pal Singh. "Bankruptcy Prediction Using The Altman Z-Score Modification Model in India: A Case Study of Bharti Airtel Limited." Southeast Asian Business Review 3, no. 1 (2025): 12–33. https://doi.org/10.20473/sabr.v3i1.63958.

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Indian telecom sector is facing intense competition since Reliance Jio has entered the telecom market. Furthermore, the COVID-19 pandemic has further strained the sector. Considering these, evaluating a company's financial health is of paramount importance. In spite of various methods available for determining financial stability, Altman's Z-score modification model has been considered a better tool to forecast the possibilities of bankruptcy and determine financial viability of a company. Therefore, this model has been adopted to track Bharti Airtel’s financial health in light of the aforemen

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Muhlis, Muhlis. "PENERAPAN MODEL Z-SCORE UNTUK PREDIKSI KEBANGKRUTAN BANK BRI SYARIAH TAHUN 2014-2016." DIKTUM: Jurnal Syariah dan Hukum 16, no. 1 (2018): 81–97. http://dx.doi.org/10.35905/diktum.v16i1.523.

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Abstract: The application of the z-score model is done to find out the early condition possible to avoid the failure of bank management when experiencing financial difficulties that will trigger potential bankruptcy if the score category is below 2.99. The method used is the Altman Z-Score model by analyzing the financial statements of PT Bank BRI Syariah from 2014-2016. Based on the results of the research conducted, the z-score in 2014 was 5.13 and 6.24 in 2015, while in 2016 it was 5.24. Altman's score results indicate that the company is free from potential bankruptcy. The debt ratio has a

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7

Masduki, Uki, Adi Rizfal Efriadi, and Ermalina Ermalina. "Kemampuan Model Z- Score dan Model Springate Dalam Memprediksi Financial Distress BPR Multi Artha Sejahtera." Jurnal Manajemen dan Keuangan 8, no. 1 (2019): 68–79. http://dx.doi.org/10.33059/jmk.v8i1.1156.

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The purpose of this study was to test the ability of the model or analytical tool used to predict the bankruptcy of the company, namely the Altman's Z-afternoon model and the Springate model of BPR Multi Artha Sejahtera whose license has been revoked by the Financial Services Authority (OJK) through Commissioner Decree Number 16 / KDK.03 / 2016 with company considerations deteriorating. The data used is secondary data, namely the 2012-2015 BPR Multi Artha Sejahtera financial report data obtained from Bank Indonesia reports. The data is then analyzed using the Altman Z-score (Z-Score) and Sprin

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8

Dr., Vinay Kumar. "Predicting Financial distress of Pharmaceutical Companies in India using Altman Z Score Model." Predicting Financial distress of Pharmaceutical Companies in India using Altman Z Score Model 8, no. 10 (2023): 4. https://doi.org/10.5281/zenodo.10184222.

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The present paper aims to measure the financial distress of top five pharmaceutical companies operating in India using Altman's Z score model. This model is widely used throughout the world to identify financial distress in the company. The classical z score model is based on 5 ratios i.e. working capital to total asset ratio, retained earnings to total asset ratio, EBIT to total asset ratio, market value of equity to total liabilities and sales to total asset ratio. The study concluded that four companies fall in the Safe zone according to z score. These companies with average z score of last

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9

Pravin, Pratik, and Drashti Anantbhai Dhabaliya. "ANALYSIS OF FINANCIAL DISTRESS USING ALTMAN'S Z-SCORE MODEL IN SELECTED INDIAN PHARMACEUTICAL COMPANIES." Journal of Advanced Research in Economics and Administrative Sciences 4, no. 4 (2023): 1–13. http://dx.doi.org/10.47631/jareas.v4i4.626.

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The necessity for financial analysis is growing as a result of industrialization, trade, and commerce. Predicting financial difficulties has become a key concern for all firms since the financial crisis happened in 2008. For assessing a company's financial health and the likelihood of insolvency, employ Altman’s Z-score model. Early bankruptcy prediction is vital for both the parties involved in the company and society. Based on their sales in the year 2021-2022, the top five pharmaceutical companies listed on the Bombay Stock Exchange were chosen as a sample for this study. The period of the

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Milašinović, Marko, Snežana Knežević, and Aleksandra Mitrović. "Bankruptcy forecasting of hotel companies in the Republic of Serbia using Altman's Z-score model." Menadzment u hotelijerstvu i turizmu 7, no. 2 (2019): 87–95. http://dx.doi.org/10.5937/menhottur1902087m.

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11

Chaturvedi, Krishna Murari, and Mohit Mehta. "Altman Z-Score Model Effects of Non-Performing Assets (NPAs) in Public and Private Bank In India." Think India 22, no. 2 (2019): 1431–37. http://dx.doi.org/10.26643/think-india.v22i2.9009.

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The study aims to measure the effectiveness of Altman's Z-score model using Non-Performing Assets (NPA) as a level fixture index. Paper examines if Altman's Z-score model captures the decline in the financial health of the banks caused by the NPAs. The sample size is taken in selected two public (CBI & PNB) & two private sector banks (ICICI & HDFC). The study is based on secondary data & data will be collected for the last five years 2014 to 2018. The findings of this paper suggest that, due to the uniqueness of the Indian banking sector during the NPA crisis, the 'Emerging Mar

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Marsenne, Maureen. "ANALISA PENGGUNAAN ALTMAN’S Z-SCORE UNTUK MEMPREDIKSI KEBANGKRUTAN PERUSAHAAN (STUDI KASUS PADA PT. BANK PERMATA, Tbk)." Balance: Media Informasi Akuntansi dan Keuangan 12, no. 2 (2020): 56–74. http://dx.doi.org/10.52300/blnc.v12i2.1884.

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Altman set thresholds for measurement with the Z Score, all companies those with a Z Score greater than 2.99 are classified as non-bankrupt companies. Companies that have a Z score between 2.7 to 2.99 show little indication problem (though not serious). Companies that have a Z Score between 1.8 to 2.69 gives an indication if the company does not make radical improvements, the company may experience the threat of bankruptcy within two years and, Z Score below 1.8 shows an indication that the company is facing the threat of bankruptcy seriously and investors and creditors should be careful in ma

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13

Kudakwashe, MAVENGERE. "2015-10-30." International Journal of Management Sciences and Business Research 4, no. 10 (2015): 08–14. https://doi.org/10.5281/zenodo.3461599.

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The study sought test the validity of the Atlman Z Score (bankruptcy prediction) and the Beneish M score (earnings manipulation) as investment models that can be adopted in entity financial statements analysis by stakeholders. The study utilised financial statements obtained from entity Z’s website from the periods 2011 to 2014. The results reveal entity as in the “grey zone” using the Altman Z Score model in 2011 whilst 2012 to 2014 discovers financial distress. The Beneish m score reveals entity Z an earnings manipulator for 2010 and 2014 with m scores of -2.11 and -0.10. D

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Jayadev, M. "Predictive Power of Financial Risk Factors: An Empirical Analysis of Default Companies." Vikalpa: The Journal for Decision Makers 31, no. 3 (2006): 45–56. http://dx.doi.org/10.1177/0256090920060304.

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This paper provides empirical evidence on the significance of financial risk factors in predicting default companies. Traditionally, credit decision process is built on accounting ratios derived from financial statements of the borrower. Combining various ratios through application of multivariate statistical techniques and testing their predictive power has been popular in credit risk quantification. Altman's Z-score model is the most acceptable model in this category. In this paper, three forms of Z-score models are applied: The first equation is developed by surveying the internal credit ra

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Anggun Minati, Agnes, Gustati, and Hidayatul Ihsan. "Analisis Perbandingan Prediksi Kebangkrutan Bank Syariah Dan Bank Konvensional Menggunakan Altman’s EM Z- Score Model." Akuntansi dan Manajemen 11, no. 2 (2016): 1–25. http://dx.doi.org/10.30630/jam.v11i2.90.

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The development of Islamic banks in Indonesia increasingly shows that Islamic banks are ready to become competitors for conventional banks. Islamic banks that use margin systems or profit sharing whereas conventional banks use interest systems give rise to different expectations. It is possible to have a difference in terms of bankruptcy prediction using the Altman EM Z-Score Model ratio. The Altman EM Z-Score Model is a modification of the Altman ratio in 2002 that is used for non-manufacturing companies and has not gone public. This final project aims to analyze the comparison of predictions

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16

Thang, Do Nang, and Nguyen Duc Duong. "Combined S&P and Z-score research in credit assessment of Vietnamese commercial banks." International Journal of Innovative Research and Scientific Studies 8, no. 1 (2025): 2723–31. https://doi.org/10.53894/ijirss.v8i1.5047.

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Economists often refer to banking as the "business of risk." Indeed, no industry presents risks as significantly as the monetary-credit sector. Banks face risks not only from their subjective causes but also from those caused by customers. Therefore, "the credit risk of banks is not merely additive but could be a multiplicative factor of the economy's risks." Given this critical role, this paper proposes a combination of a credit rating solution with Altman's Z-Score model (Altman [1]) and a scoring method to provide early warnings for corporate credit risks. This approach aims to equip commer

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Knežević, Snežana, Marko Špiler, Marko Milašinović, Aleksandra Mitrović, Stefan Milojević, and Jovan Travica. "Using Beneish M-Score and Altman Z-Score models to detect financial fraud and company failure." Tekstilna industrija 69, no. 4 (2021): 20–29. http://dx.doi.org/10.5937/tekstind2104020k.

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Bankruptcy is a risk that any company can face, regardless of its size. The importance of predicting a company's bankruptcy for years before its development is enormous, and it is important for financial sustainability. Financial reporting is an important platform for making financial decisions of investors and creditors. In recent years, the frequency of false financial reporting by firms has increased and there are concerns about investors' confidence in capital market. Academics and industry experts adopt a variety of risk management techniques to detect fraudulent financial reporting. A ca

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18

Rahman, Syed Mohammad Khaled, Md Khairul Islam, and Md Mofazzal Hossain. "LEVERAGE INDUCED FINANCIAL DISTRESS OF MANUFACTURING FIRMS IN BANGLADESH: A COMPARISON BETWEEN LISTED MNCS AND DOMESTIC FIRMS BY APPLYING ALTMAN’S Z SCORE MODEL." Indian Journal of Finance and Banking 5, no. 2 (2021): 28–43. http://dx.doi.org/10.46281/ijfb.v5i2.1006.

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Financial distress arises from excessive debt capital. The purpose of the study was to determine Altman's Z score and analyze as well as compare the effect of debt on Z scores of listed MNCs & domestic companies of Bangladesh over 24 years (1996-2019). The study was based on secondary data. Seven local companies and seven MNCs were selected as a sample from six manufacturing industrial sectors. It was found that on average one local firm was in the grey zone and the rest 13 firms were in the safe zone (Z scores>2.99). MNCs’ Z scores were significantly higher than that of domestic compan

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19

Milić, Dragan, Dragana Tekić, Vladislav Zekić, Tihomir Novaković, Milana Popov, and Zlata Mihajlov. "Bankruptcy prediction models for large agribusiness companies in AP Vojvodina." Ekonomika poljoprivrede 68, no. 3 (2021): 805–22. http://dx.doi.org/10.5937/ekopolj2103805m.

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The aim of this paper is to present application of different methods used for predicting bankruptcy of large agricultural and food companies in AP Vojvodina, as well as to determine which model is the most suitable for analyzing the companies from the observed sectors. The following three models were applied in the paper: Altman's Z'-score model, Kralicek DF indicators and Kralicek Quick test. The analysis included five companies from the agricultural sector and five companies from the food sector operating on the territory of AP Vojvodina in the period from 2015 to 2019. The results of the re

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Joshi, Manoj Kumar. "Financial performance analysis of select Indian Public Sector Banks using Altman's Z-Score model." SMART Journal of Business Management Studies 16, no. 2 (2020): 74. http://dx.doi.org/10.5958/2321-2012.2020.00018.4.

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Muñoz‐Izquierdo, Nora, Erkki K. Laitinen, María‐del‐Mar Camacho‐Miñano, and David Pascual‐Ezama. "Does audit report information improve financial distress prediction over Altman's traditional Z ‐Score model?" Journal of International Financial Management & Accounting 31, no. 1 (2019): 65–97. http://dx.doi.org/10.1111/jifm.12110.

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Milić, Dragan, Nedeljko Tica, Vladislav Zekić, et al. "The assessment of bankruptcy risk of companies from the sector of drying and storage of crops and plants." Journal on Processing and Energy in Agriculture 25, no. 3 (2021): 86–90. http://dx.doi.org/10.5937/jpea25-32138.

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Contemporary market conditions make all business entities confront various risks - credit risk, liquidity risk, cash flow risk and market risk. Companies should predict these particular business risks and manage them adequately in order to minimize their influence. The assessment of creditworthiness and bankruptcy risk has a major role in the process of prediction and management of company risks. The creditworthiness of companies presents their ability to meet financial obligations to their creditors contemporary business conditions recognize several methods for assessment of credit ability of

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Bondar, Ros Bunga, and Mochamad Kohar Mudzakar. "Bankruptcy Prediction Model: Altman (Z-Score), Springate (S-Score), Zmijewski (X-Score) And Grove (G-Score) Models in Companies Listed at The LQ 45 Stock Index." Jurnal Ekonomi, Bisnis & Entrepreneurship 17, no. 2 (2023): 279–95. http://dx.doi.org/10.55208/jebe.v17i2.435.

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This study aims to assess the effectiveness of four prominent bankruptcy prediction models, namely Altman's Z-Score, Springate's S-Score, Zmijewski's X-Score, and Grove's G-Score, in forecasting financial distress among companies listed on the LQ 45 Stock Index. The research leverages financial data spanning a specified period to construct and evaluate the predictive capabilities of these models. By employing a sample of companies operating within the LQ 45 index, the study provides a comprehensive comparative analysis of the models' accuracy, sensitivity, and specificity in identifying firms

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Divekar, Vivek, and Sreerupa Sukhari. "Assessing the Financial Soundness of Indian Aviation Sector Companies by Using Altman's Z-Score Model & Pilarski's P- Score Model." Universal Journal of Accounting and Finance 9, no. 6 (2021): 1222–34. http://dx.doi.org/10.13189/ujaf.2021.090602.

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Naullage and Sudasinghe. "BANKRUPTCY PREDICTION ON FIRM PERFORMANCE IN PRE – DURING COVID -19 PANDEMIC: SPECIAL REFERENCE TO LISTED MAINBOARD HOTEL COMPANIES IN SRI LANKA." Journal of Accountancy & Finance 10, no. 1 (2023): 105–27. http://dx.doi.org/10.57075/jaf1012306.

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The influence of corporate bankruptcy on the economy is considerable as it encompasses shareholders, financial lenders, operational lenders, and the government. It is necessary to do a bankruptcy evaluation so the businesses can receive early bankruptcy warning signs. The earlier signs of insolvency are identified, the better for management because they can take immediate action to correct the issue. This paper aims to investigate the impact of bankruptcy prediction on firm performance with involving COVID -19 pandemic with a focus on listed mainboard-hotel companies in Sri Lanka while referri

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Hamid, Guleshan Mohsin, Gulizar Abdullah Mohammed, Kurdistan M. Taher Omar, and Shelan Mohammed Rasheed Haji. "Using Altman and Sherrod Z- Score Models to Detect Financial Failure for the Banks Listed on the Iraqi Stock Exchange (ISE) Between 2009 – 2013." International Journal of Professional Business Review 8, no. 4 (2023): e01329. http://dx.doi.org/10.26668/businessreview/2023.v8i4.1329.

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Purpose: The Purpose of the study was to examine the validity of the Altman Z- Score and Sherrod Z- Score models in financial failure prediction. To achieve the study's goal, references from various authors who have reviewed this topic were used. Theoretical framework: The study highlights the importance of analyzing and delving into the various notions of financial failure and distress. When it comes to potential effects on the wealth of creditors, stockholders, and society as a whole, academics and researchers consider a company's distress and bankruptcy to be the most important issue to be

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Bondar, Ros Bunga, and Mochamad Kohar Mudzakar. "Bankruptcy Prediction Model." Jurnal Ekonomi, Bisnis & Entrepreneurship 17, no. 2 (2023): 279–95. https://doi.org/10.55208/fksmm983.

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This study aims to assess the effectiveness of four prominent bankruptcy prediction models, namely Altman's Z-Score, Springate's S-Score, Zmijewski's X-Score, and Grove's G-Score, in forecasting financial distress among companies listed on the LQ 45 Stock Index. The research leverages financial data spanning a specified period to construct and evaluate the predictive capabilities of these models. By employing a sample of companies operating within the LQ 45 index, the study provides a comprehensive comparative analysis of the models' accuracy, sensitivity, and specificity in identifying firms

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Iswandi, Iswandi. "Analisis Potensi Kebangkrutan PT. Berlian Laju Tanker, Tbk. dengan Menggunakan Altman’s Z Score." Binus Business Review 3, no. 2 (2012): 654. http://dx.doi.org/10.21512/bbr.v3i2.1350.

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PT. Berlian Laju Tanker, Tbk. (BLTA) is a company engaged in the ocean transportation services listed on the Indonesia Stock Exchange and the Singapore Stock Exchange. In 2009 and 2010 BLTA experienced a net loss. At the end of 2011 the company rocked the financial markets in Indonesia and Singapore being unable to meet financial obligations to financial institutions and corporate bondholders. Given such conditions until the end of August 2012 BLTA can not submit audited financial statement of year 2011 to the authorities of stock exchange and public. By using the 2007 to 2010 audited financia

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Singh, Gurmeet, and Ravi Singla. "Prediction of financial distress in the Indian corporate sector: an improvement over Altman's Z-score model." International Journal of Business Continuity and Risk Management 13, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbcrm.2023.10055472.

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Singh, Gurmeet, and Ravi Singla. "Prediction of financial distress in the Indian corporate sector: an improvement over Altman's Z-score model." International Journal of Business Continuity and Risk Management 13, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbcrm.2023.130287.

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Surono, Yunan, and Sindy Dwiroro Pangestu. "Analisis Kinerja Keuangan Sektor Manufacture dengan Multiple Discriminant Analysis Bankcruptcy Model di Bursa Efek Indonesia Periode 2014 – 2017." J-MAS (Jurnal Manajemen dan Sains) 4, no. 2 (2019): 298. http://dx.doi.org/10.33087/jmas.v4i2.111.

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This research aims to prove the financial performance of Manufacture sector with Multiple Discriminant Analysis Bankruptcy Model when measured using the Altman's Z-score first model, Altman's Z-score revision and Altman's Z-score modifications, Springate model and Zmiejewski model as well as to get the stocks that have the best performance based on the model. In this research the population used is the stock sector manufacturing group on the Indonesia Stock Exchange in the year 2014 – 2017 as many as 166 issuers manufacture. This research sample is the manufacture sector company, which provide

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De Mel, L. M. N. S., and H. J. R. Buddhika. "The Impact of Financial Distress on Financial Performance: Evidence from Listed Licensed Finance Companies in Sri Lanka." Journal of Desk Research Review and Analysis 2, no. 2 (2025): 40–62. https://doi.org/10.4038/jdrra.v2i2.41.

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In the ever-changing world of financial markets, the intersection between financial distress and performance is important. Financial distress, an entity's inability to satisfy its financial obligations, can seriously affect a company or individual's market value and stability. Sri Lanka's financial sector has recently faced difficulties, with certain institutions failing due to insolvency or regulatory noncompliance. The research intends to give significant insights to stakeholders, regulators, and investors in the Sri Lankan finance industry by utilizing Altman's Z-score model. This study exa

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Altman, Edward I., Małgorzata Iwanicz-Drozdowska, Erkki K. Laitinen, and Arto Suvas. "Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z- Score Model." Journal of International Financial Management & Accounting 28, no. 2 (2016): 131–71. http://dx.doi.org/10.1111/jifm.12053.

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Nusyirwan, Nusyirwan, Yayu Kusdiana, Lisa Tinaria, Zubir Zubir, and Masril Masril. "Model Altman’s Z-Score dan Springate Memprediksi Financial Distress (Studi Kasus Perusahaan Sub Migas yang Tercatat di BEI Tahun 2017 - 2020)." Jurnal Akuntansi, Manajemen, Bisnis dan Teknologi (AMBITEK) 3, no. 1 (2023): 127–35. http://dx.doi.org/10.56870/ambitek.v3i1.78.

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The increase in the average need for oil and gas for both the public and the industrial sector has forced Indonesian oil and gas companies to look for new oil reserves in the midst of declining oil and gas production from year to year. If Indonesian sub-oil and gas companies cannot survive in these conditions, they are likely to experience losses that will affect their financial position and will eventually face financial distress or be threatened with bankruptcy. The purpose of this research is to determine the prediction of bankruptcy in sub-oil and gas companies in Indonesia that are listed

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Dr, Kyriazopoulos Georgios, and Georgios Mavrommatis. "Predicting Bankruptcy on Oil Companies Before and After the Pandemic Using Two Altman's Z-Score Models. Industrial and Emerging Markets. Evidence from Greece." Journal of Economics, Finance and Management Studies 06, no. 11 (2023): 5724–30. https://doi.org/10.5281/zenodo.10301100.

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Purpose This paper is prepared with the aim of establishing whether there is a possible bankruptcy of the Greek oil companies Listed on the Athens Stock Exchange in the last six years 2016-2021. This period under consideration also includes the two years of the 2019-2020 pandemic. Design/methodology/approach -The effort of this study is carried out using the Altman Z-Score model bankruptcy model for Emerging Markets. This paper focuses on bankruptcy, a situation which is a consequence of financial failure. Bankruptcy can happen to any organization or government body. The consequences of bankru

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Kantar, Lokman, and Ayşegül Ertuğrul Ayrancı. "Estimating Financial Failure in Businesses Using Artificial Neural Networks: Turkish Manufacturing Industry Model Study." Journal of Corporate Governance, Insurance, and Risk Management 9, no. 2 (2022): 327–40. http://dx.doi.org/10.56578/jcgirm090203.

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Businesses need to be financially successful to achieve sustainable growth and maximise firm value. The financial failure of businesses is a situation that is carefully monitored by business managers, shareholders of the business, financial institutions that lend to the business, and the government. For this reason, in this study, the financial failure of 153 manufacturing companies operating in Turkey and traded on Borsa Istanbul has been tried to be estimated. In the research, the annual financial statements between the years 2009-2021 were used and artificial neural networks were preferred

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Guzikova, Liudmila, Natalia Neelova, and Denis Ushakov. "Environmental analysis in bankruptcy diagnosing (the case of construction companies)." E3S Web of Conferences 371 (2023): 02059. http://dx.doi.org/10.1051/e3sconf/202337102059.

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The instability of the economic situation, combined with the systematically important nature of the construction industry, attracts attention to the financial viability of construction companies constituting the basis for the successful implementation of their activities in market conditions. The article examines the bankruptcy statistics of Russian construction companies, analyzes the issues of assessing and predicting insolvency and discusses the utility of applying well-known models, in particular, the Altman’s modified five-factor model, in Russian situation. The dynamics of the financial

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Nurmansyah, Wahyudi, Afdal Mazni, and F. Febriyanto. "Comparison analysis of Altman's and Foster's Z-score model in predicting bankruptcy: evidence from Indonesian automotive and component industries." Diponegoro International Journal of Business 4, no. 2 (2021): 126–35. http://dx.doi.org/10.14710/dijb.4.2.2021.126-135.

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The purpose of this study is to determine differences in bankruptcy predictions of the Altman and Foster models. The sampling technique used is purposive sampling with an observation period of 2016-2018 with 12 company samples per year. The analysis technique uses the Altman and Foster method with the paired two-sample t-test as a hypothesis test tool. Based on the results of the Altman and Foster Z- Score model of automotive and component industries in 2016-2018 it can be concluded that the financial model of Altman and Foster can predict bankruptcy. Furthermore, the hypothesis testing found

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Hamzah, Ruth Samantha, and Mutiara Lusiana Annisa. "Altman’s Z”-Scores for financial distress predictions among food and beverages industry in Indonesia." Owner 6, no. 1 (2022): 1056–68. http://dx.doi.org/10.33395/owner.v6i1.696.

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The food and beverage industry is an accelerated sector in Indonesia. They are predicted to be one of the pivotal sectors supporting manufacturing and economic growth. This study aims to predict financial distress on food and beverage industries in Indonesia using Altman's Z”-Score modification model. The samples of this study are 48 firm-year observations in the period of 2018 and 2019. The results show there are 2.1 percent of firms experienced financial distress (Z < 1.10), 2.1 percent of firms was in grey area, i.e., prone to financial distress (Z < 1.10 < 2.60), and 95.8 percent

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Lahmar, Maroua, and Ibtissem Belkhadem. "Gazprom's financial stability and geopolitical challenges (2008-2023): LNG expansion as a strategic response." ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, no. 1 (June 2025): 125–46. https://doi.org/10.3280/efe2025-001005.

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This paper examines Gazprom's financial health (2008-2023) using Altman's Z-score model, analyzing key financial ratios and their relation to Russian natural gas exports amidst geopolitical upheavals. The findings reveal significant volatility in financial performance, particularly during the financial crisis of 2008 and the Russian-Ukrainian conflicts, highlighting the strain on Gazprom's financial position. Despite maintaining a strong overall financial standing, recent declines indicate vulnerabilities linked to reduced pipeline exports to Europe. The study emphasizes the potential of LNG e

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Shohel, Rana, and Kamruzzaman A.S.M. "Measurement and Comparison of the Financial Soundness of Conventional and Islamic Commercial Banks in Bangladesh using the Altman's Z Score Model." International Journal of Science and Business 5, no. 12 (2021): 52–62. https://doi.org/10.5281/zenodo.5800454.

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The prime objective of the paper is to measure and compare the financial soundness of conventional and Islamic commercial banks in Bangladesh and to suggest ways to improve the financial status of both categories of banks. Altman’s z-Score model is used to measure and predict the financial soundness of selected banks while an independent sample t-test is also used to measure the gap in financial soundness between conventional and Islamic commercial banks. This study uses secondary data and covers ten years study period of 2010-2019. The major finding of the study is that all conventional

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Muhammad Tariq, Muhammad Hasan, Aurangzeb Khanji, and Atif Aziz. "Investigating the Factors Predicting the Financial Distress of Firms: A Study Based on Pakistani Firms." Critical Review of Social Sciences Studies 3, no. 1 (2025): 1381–93. https://doi.org/10.59075/tc5e5k25.

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This study tries to investigate the determinants of financial distress or bankruptcy prediction of the non-financial firms listed on the Pakistan Stock Exchange 100 Index. The study has taken a sample of 21 non-financial firms representing the cement, sugar, and textile sectors for analysis in the period spanning from 2014 to 2023. Altman's Z-score is considered as proxy for financial distress. This study applies the Panel Least Squares (PLS) approach to test the hypotheses besides the Fixed Effect (FEM) and Random Effect (REM) models. A comparative assessment of PLS, FEM, and REM was also per

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Drezner, Zvi, George A. Marcoulides, and Mark Hoven Stohs. "Financial applications of a Tabu search variable selection model." Journal of Applied Mathematics and Decision Sciences 5, no. 4 (2001): 215–34. http://dx.doi.org/10.1155/s1173912601000165.

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We illustrate how a comparatively new technique, a Tabu search variable selection model [Drezner, Marcoulides and Salhi (1999)], can be applied efficiently within finance when the researcher must select a subset of variables from among the whole set of explanatory variables under consideration. Several types of problems in finance, including corporate and personal bankruptcy prediction, mortgage and credit scoring, and the selection of variables for the Arbitrage Pricing Model, require the researcher to select a subset of variables from a larger set. In order to demonstrate the usefulness of t

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Dissanayake, D. M. S. B. "Impact of Financial Distress on Financial Performance: Evidence from Manufacturing Companies in the Colombo Stock Exchange." International Journal of Latest Technology in Engineering, Management & Applied Science XIII, no. II (2024): 57–64. http://dx.doi.org/10.51583/ijltemas.2024.130208.

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The research investigates the Impact of Financial Distress on Financial performance: Evidence from the Manufacturing Sector in the Colombo Stock Exchange. All 36 manufacturing companies in the Colombo Stock Exchange were selected as a population. The study period is five years, from 2015 to 2019. Altman's Zscore model is used to diagnose the financial health of companies, and then the impact of financial distress is measured on financial performance. The study shows a significant association between financial distress and financial performance in the Manufacturing Sector on the Colombo Stock E

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Ghoni, Muhammad Abdul, Asep Maksum, Abdul Qadir Jaelani, and Noor’ain Mohamad Yunus. "THE PREDICTION OF FINANCIAL DISTRESS IN PROPERTY COMPANIES IN SOUTHEAST ASIAN: INDONESIA, SINGAPORE, AND MALAYSIA." Jurnal Ekonomi dan Bisnis Airlangga 35, no. 1 (2025): 129–46. https://doi.org/10.20473/jeba.v35i12025.129-146.

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Introduction: This study aims to assess and evaluate the financial distress state of a sample including 204 data points from 35 enterprises in Southeast Asia, specifically Indonesia, Malaysia, and Singapore. Methods: This study analyzes the effects of Covid-19 on these enterprises, specifically concentrating on the likelihood of financial hardship as assessed by Edward Altman's Z-score model. Results: Evidence indicates that property businesses listed on the Indonesia, Malaysia, and Singapore Stock Exchanges had greater financial difficulties during the Covid-19 period compared to the post-Cov

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Gaos, Robi Ridhayatul, and Rina Mudjiyanti. "PENGARUH CORPORATE GOVERNANCE DAN FIRM SIZE TERHADAP FINANCIAL DISTRESS (Studi pada Perusahaan Perbankan yang terdaftar di Bursa Efek Indonesia periode 2017-2019)." Kompartemen : Jurnal Ilmiah Akuntansi 19, no. 1 (2021): 13. http://dx.doi.org/10.30595/kompartemen.v19i1.11218.

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This study aims to find empirical evidence of the influence of corporate governance and firm size on financial distress. The sample used in this study is a banking company listed on the Indonesia Stock Exchange (BEI) for the 2017-2019 period. The sampling technique used was purposive sampling and obtained a sample of 40 samples that met the criteria. The data analysis technique used is multiple regression analysis. The financial distress criteria in this study measured using the Z-score in Altman's financial distress prediction model. Based on the study results, it can be concluded that manage

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Rane, Jayantkumar Vijay. "Adequateness of Applying Altman's Z Score Model for Prediction of Potential Financial Distress in NBFIs of India: A Case Study of Reliance Capital Ltd." Sanshodhan 11, no. 1 (2022): 102. http://dx.doi.org/10.53957/sanshodhan/2022/v11i1/169801.

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Yang, F., and Y. Bian. "PNS58 ANALYSIS OF FINANCIAL STATUS AND DETERMINANTS WITH ALTMAN'S Z-SCORE MODEL: A PANEL DATA INVESTIGATION FROM TRADITIONAL CHINESE MEDICINE LISTED COMPANIES IN CHINA." Value in Health 22 (November 2019): S772. http://dx.doi.org/10.1016/j.jval.2019.09.1960.

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Zhao, Xingli, Wenjie Wang, Guochao Liu, and Vinay Vakharia. "Optimizing Financial Risk Models in Digital Transformation-Deep Learning for Enterprise Management Decision Systems." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/joeuc.342113.

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The digital transformation of enterprises has amplified the complexity of financial risks, underscoring the significance of optimizing financial risk warning models to ensure sustainable development. This study integrates several deep learning techniques, including Back Propagation Neural Network (BPNN), Bi-Long Short-Term Memory (Bi-LSTM), and transfer learning, to enhance the risk warning system and improve the accuracy and efficiency of financial risk prediction models. The results demonstrate that the proposed algorithm surpasses the baseline models in various metrics. For instance, on the

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Tekić, Dragana, Beba Mutavdžić, Nebojša Novković, Tihomir Novaković, and Nataša Vukelić. "Analysis of the financial position of mill companies in Vojvodina." Journal on Processing and Energy in Agriculture 24, no. 3-4 (2020): 119–22. http://dx.doi.org/10.5937/jpea24-30296.

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The subject of this research is the financial position of mill companies in Vojvodina. The specific aim of the research is to analyze the is to compare the applied models for the analysis of the financial position. The basis of the analysis are the financial reports of the observed companies in the period from 2015 to 2019. The analysis of the financial position was performed using two models: Altman's Z-score model and Kralicek's Quick test model. These models use standard financial ratio indicators to assess the risk of bankruptcy of companies, i.e. their financial stability and profitabilit

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