Altman Z-Score Research Articles

The Use of Altman Z-Score, Springate, and Zmijewski in Detecting Financial Distress in the Transportation and Logistics Sector Companies

This research aims to explore how the Altman model (Variable X1), the Springate model (Variable X2), and the Zmijewski model (Variable X3) affect financial distress. A quantitative approach is used, with information gathered from secondary sources, specifically financial statements of transportation and logistics firms on the Indonesia Stock Exchange. The analysis includes systematic compilation, tabulation, and examination of data. The findings from the discriminant analysis indicate that only the Springate model (X2) demonstrates statistical significance, whereas both the Altman (X1) and Zmijewski (X3) do not achieve significance. In terms of predictive accuracy for each model, Altman yields 54.16%, Springate provides 58.33%, and Zmijewski records 16.66%. In summary, within companies operating in the transportation and logistics sector, it is evident that Springate model (X2) possesses a substantial influence in forecasting instances of financial distress, while neither the Altman (X1) nor Zmijewski (X3) models exhibit a comparable level of influence. The findings indicate that the Springate model is the most effective tool for predicting financial distress in this sector.

Determinan Financial Distress Pada Perusahaan Bumn Di Bursa Efek Indonesia

This study is a quantitative research that aims to determine the influence of liquidity, profitability, leverage, and sales growth on financial disclosure in state-owned companies on the Indonesia Stock Exchange for the period 2018-2022. Financial distress is a condition of a company where its finances are experiencing an unhealthy state or crisis, the measurement of financial distress uses the Altman Z-Score model, the population in this study is state-owned companies listed on the Indonesia Stock Exchange (IDX), with a sample of 10 State-Owned Enterprises (SOE) obtained by the purposive sampling method. The processing and testing technique uses multiple linear regression tests. The results of the study show that sales growth has no effect on financial distress. Meanwhile, liquidity has a significant negative effect, profitability has a significant positive effect and leverage has a significant negative effect on financial distress.

Investigating the Factors Predicting the Financial Distress of Firms: A Study Based on Pakistani Firms

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 performed in comparing the model performance. The results indicate that the liquidity, efficiency, and solvency ratios are positively associated with the financial distress measured using the Altman Z-score. On the other hand, leverage and firm size show a very important negative relationship with financial distress; this may further mean that even large firms may become distressed due to other contributing factors. Profitability, as represented by profit margins, does not show any significant impact on financial distress in this study. The study underscores the importance of using bankruptcy prediction models to enable firms to identify likely financial distresses early. Companies also need to look forward toward other factors that may cause distress and focus on if cash flow and debt management practices need improvement to meet their financial obligations efficiently.

Analisis Financial Distress Pada Perusahaan Sektor Ritel yang Terdaftar di Bursa Efek Indonesia Periode 2020-2023

The development of the retail sector is influenced by many factors such as competition, government policies and technology that have an impact on consumer patterns in consuming goods or services. This will affect the company's business activities, especially in gaining profits. This will certainly have an impact on financial performance. Poor financial conditions allow companies to face financial difficulties and even lose investor trust. The purpose of this study was to determine the overall level of financial difficulties in retail sector companies during the 2020-2023 period using the Altman Z-Score Modified method to predict financial difficulties. This study uses a non-statistical quantitative method and 12 companies were used as samples using a purposive sampling technique. The results showed that out of 12 companies, one company experienced financial distress, one company was in the gray area zone and ten companies were in the healthy category.

Bankruptcy Analysis Using the Altman Z-Score Method

Bankruptcy is a state in which a company is unable to fulfil its financial obligations or a situation where the corporation initially functions but thereafter fails in business management. Bankruptcy is a state in which a firm lacks the money to operate its operation. The objective of the research is to identify the variables contributing to bankruptcy in PT. Garuda Indonesia. This research employs the Altman Z-score methodology using a bankruptcy calculation. This analysis indicates that the firm is at risk of bankruptcy, since its present assets from 2016 to 2019 are insufficient to meet its financial obligations. Companies must use deliberate, clear, and suitable measures to enhance operational cost efficiency. An inadequate business plan and human resources without a clear vision and goal for the organisation contribute to losses. This research seeks to evaluate the financial performance of PT Garuda Indonesia (Persero) Tbk from 2016 to 2019 using the Altman Z-Score model. The population and sample in this research consist of the complete financial statements of PT Garuda Indonesia (Persero) Tbk for the years 2016 to 2019. The findings of this research indicate that from 2016 to 2019, the bankruptcy rate at PT Garuda Indonesia (Persero) Tbk was unfavourable, as shown by a Z-Score below 1.10, signifying a state of bankruptcy. The most pronounced decrease was seen in the Working Capital to Total Asset ratio, particularly in 2019. This results from the annual growth in current obligations.

Determinants of Corporate Tax Avoidance: The Role of Financial Distress, Profitability, and Capital Intensity in Emerging Markets

This study investigates the influence of financial distress, profitability, and capital intensity on tax avoidance among companies listed on the Egyptian stock market from 2020 to 2022. Utilizing a quantitative approach, the research analyzes secondary data from 36 firms that maintained profitability during the study period. Tax avoidance is measured using the Current Effective Tax Rate (CETR), while financial distress, profitability, and capital intensity are assessed through the Altman Z-score, Return on Assets (ROA), and capital intensity ratio, respectively. Multiple regression analysis reveals that profitability has a significant negative effect on tax avoidance, suggesting that highly profitable firms are less inclined toward aggressive tax planning. Conversely, financial distress and capital intensity exhibit no significant relationship with tax avoidance, underscoring the context-specific dynamics in an emerging market like Egypt. These findings contribute to the growing body of literature on corporate tax behavior by highlighting the unique determinants of tax avoidance in developing economies. Policymakers are encouraged to consider these insights when designing tax regulations that promote compliance while addressing economic disparities. Future research should expand on these results by incorporating broader datasets and exploring the role of corporate governance and institutional factors in shaping tax strategies

Analyzing The Effect of Financial Ratio on Financial Distress Using The Logistic Regression Method in Manufacturing Companies

Financial distress is a company that has difficulty paying its obligations, so it cannot carry out business as usual and may experience bankruptcy. This study aims to analyse the factors that influence the possibility of financial distress by considering financial ratios as indicators to predict the occurrence of financial distress in manufacturing companies in Indonesia. Four independent variables which are financial ratios include Current Ratio, Debt Ratio, Return on Assets (ROA), and Working Capital Turnover. In comparison, the dependent variable is financial distress. This study uses the Altman Z-score model and the data analysis method used is logistic regression analysis. Where logistic regression is one of the statistical analysis methods used to represent the relationship between independent variables and dependent variables containing nominal and ordinal data. The population used in this study includes manufacturing companies listed on the Indonesia Stock Exchange (IDX) in the 2015-2019 period. The sample was determined by the purposing sampling technique. The results showed that not all financial ratios can have a significant effect on the occurrence of Financial Distress. In the results that have been analysed, it is found that Current Ratio does not have a significant positive effect on Financial Distress, Debt Ratio does not have a significant effect on Financial Distress, Return on Assets (ROA) has a significant positive effect on Financial Distress, and the last financial ratio Working Capital Turnover has a significant negative effect on Financial Distress.

Analysis of financial difficulties in financial management with ALTMAN Z-SCORE, ZMIJEWSKI, and SPRINGATE models in small textile and apparel companies. Listed on the Indonesia Stock Exchange

Financial difficulties are the first step experienced by a company in the bankruptcyprocess. If the company fails, many parties are harmed. Therefore, it is necessary to conduct ananalysis of financial problems or difficulties that can provide early warning for the company. Thepurpose of this study is to find out and analyze financial difficulties in financial management withthe ALTMAN Z-SCORE, ZMIJEWSKI, and SPRINGATE models in small textile and apparelcompanies listed on the Indonesia Stock Exchange (IDX) using the calculation of the AltmanZ-Score, Zmijewski, and Springate models. The research method used is quantitative descriptive research. The research population is all textile and garment subsector companies listed on the IDXas many as 25 companies. The samples were selected using a purposive sampling technique andwere obtained by 6 companies over a six-year period. The data analysis models used are the AltmanOriginal Z-Score, Zmijewski, and Springate models. This research was conducted by takingsecondary data from the Indonesia Stock Exchange website. The results of the study show thatthere are companies in the textile and garment subsector that are experiencing financial difficultiesand financial management with the model used. Zmijewski's model is the most accurate inpredicting financial difficulties with the highest accuracy of fifty percent.

Assessing the Financial Health of Urban Co-operative Banks using Altman Z-Scores

The long-term sustainability of banks is determined by measuring their financial health. This study was initiated due to the recent bank scams in the co-operative banking sector in Kerala, which raised a need to prove the financial stability of Urban Co-operative Banks (UCBs), which are an integral part of the Indian banking sector. The financial soundness of sixteen UCBs in Kerala is assessed in this study over ten years from 2012-13 to 2021-22 using Altman Z scores, re-estimated Altman Z scores using Binary Logistic Regression (BLR) and Multilayer Perceptron Neural Network (MLPNN). BLR was used to classify banks into grey and distress zones, and MLPNN was used to identify nonlinear relationships in the data. The study’s findings show that the re-estimated Altman Z score model using BLR exhibited high predictive accuracy in predicting distressed and non-distressed UCBs; therefore, it may be considered a reliable tool for predicting financial distress in UCBs. The MLPNN model needs further improvement in the inclusion of new variables, which will be included within the future scope of the research. The bank management and other stakeholders will be able to monitor the financial stability of UCBs with the help of the study findings.

Impact of Corporate Social Responsibility on Corporate Financial Sustainability During Crisis Time: A Study On EGX Companies

This paper investigates the relationship between Corporate Social Responsibility (CSR) and Corporate Financial Sustainability (CFS), with a specific focus on the moderating role of Financial Distress (FD). Employing a quantitative and deductive research design, the study examines data from companies listed on the S&P/EGX ESG Index between 2019 and 2023, a period marked by the COVID-19 pandemic and the Russian Ukrainian war. These crises provided a unique context to explore CSR’s impact on financial sustainability under economic uncertainty, particularly in Egypt, where empirical research on CSR remains sparse. The study evaluates CSR through its Environmental and Social (ES) and Governance (G) dimensions, alongside total ESG scores. Financial sustainability is measured using indicators such as Return on Assets (ROA) and Return on Equity (ROE), while financial distress is quantified using the Altman Z-score. The findings demonstrate that CSR positively influences financial sustainability, particularly in firms with higher environmental and social engagement. Governance practices, though occasionally yielding weaker direct effects, significantly mitigate risks during financial distress. Additionally, the results highlight that CSR serves as a strategic tool for enhancing resilience, stakeholder trust, and long-term financial performance. This research underscores the importance of integrating CSR into corporate strategy to achieve financial sustainability and meet societal expectations. It provides practical recommendations for corporate managers, policymakers, and investors, emphasizing CSR as a dual financial and ethical imperative. The study concludes by identifying future research directions, including sector-specific CSR impacts, longitudinal analysis, and cross-regional comparisons to further enhance the understanding of CSR’s role in corporate financial sustainability. Keywords: CSR; financial sustainability; financial distress; crisis time; EGX

Econometric analysis of cement companies in an emerging market: A sustainability outlook

Cement plays a crucial role in India’s infrastructure projects and contributes significantly to the nation’s gross domestic product (GDP) and employment. India’s urbanization relies heavily on this sector as the world’s second-largest producer of cement. Given its economic importance, understanding and assessing financial health and predicting the likelihood of default by these companies are critical for stakeholders. This study investigates various financial ratios, assesses their significance, and explores their relationship with the Altman Z score, a financial ratio used to predict the probability of a company’s bankruptcy. Recent corporate practices show a growing trend toward incorporating corporate social responsibility (CSR) reporting and sustainable growth rate (SGR) reporting into mandatory disclosures. These metrics are strategically used to improve public perception, build stakeholder trust, and convey a company’s reliability and operational performance. This study further explores the potential impact of CSR and SGR on bankruptcy risk, assessed using the Altman Z-score. Empirical analysis within the Indian cement industry reveals that, at present, CSR and SGR factors may not have a significant effect on financial distress. This study contributes significantly to the understanding of the financial health of the Indian cement industry while examining the evolving roles of CSR and SGR. It provides a nuanced perspective by empowering stakeholders to identify financial and non-financial indicators of stability. It also emphasizes the critical need for holistic assessment methodologies in the ever-evolving corporate landscape.

Altman Z-Score and Zmijewski X-Score Models on Performance to Assess the Bankruptcy Risk of Mining Companies on IDX Indonesia 2018-2022

Financial Distress Analysis is a method used to assess the financial condition of a company. This information is crucial for the company and stakeholders in the decision-making process. This research aims to project the potential financial distress of mining companies listed on the Indonesia Stock Exchange during the 2018-2022 period, using the Altman Z-Score and Zmijewski X-Score methods on financial performance. The research applied quantitative methods, with a descriptive and a different test assessment. Data analysis was carried out using the calculation of Altman Z-Score and Zmijewski X-Score using SPSS version 27. The results showed that over five years, out of 31 mining companies, the X-Score model had a significant value of 0.00 < 0.05 on performance, indicating that this method is good for use in mining companies. In conclusion, investors are advised to consider these findings when making investment decisions in the mining sector experiencing financial distress.

On the Financial Determinants of the Piotroski F-Score: An Analysis of Borsa İstanbul Firms

This study investigates the factors effective on Piotroski F-score which is a proxy for financial health of the firms. Manufacturing firms operate in Borsa İstanbul are considered in the analyses with using the time period of 2017:Q1-2024:Q3. This study contributes to the literature by identifying financial determinants of Piotroski F-Score in Türkiye. Driscoll and Kraay (1998) estimator is used with fixed effect panel regression in order to handle issues of heteroscedasticity, cross sectional dependency and autocorrelation in the model. Altman Z-score, return on invested capital, market to book ratio, Tobin’s Q ratio and Beneish M-score significantly impact Piotroski F-score. According to the regression results, companies with lower financial distress risks (higher Altman Z-score) are expected to have better financial health. Similarly, those with improved return on invested capital tend to exhibit stronger financial health. Moreover, firms with higher market-to-book ratios are generally more profitable, potentially leading to higher Piotroski F-scores, indicating better financial health. A higher Tobin’s Q value suggests greater performance expectations from the company, which correlates with higher financial health. Conversely, a negative relation between the Beneish M-score and Piotroski F-score implies that companies with a higher likelihood of earnings manipulation tend to have weaker financial health.

BIST turizm endeksinde işlem gören şirketlerin Altman Z-Skor modeli ile finansal başarısızlıklarının tahmini

Financial failure is one of the main types of failure that causes businesses to experience many problems. If timely measures are not taken to prevent financial failure, consequences that may lead to bankruptcy may emerge. Therefore, predicting possible financial failures is important for companies' future. Various models are used to predict the financial failure of companies and one of these models is Altman Z-Score Model. In this study, the financial success status of 13 companies in the BIST (Borsa Istanbul) Tourism Index was estimated using financial data for 2020-2023 with the Altman Z-Score Model. The company's financial data were obtained from the financial statements obtained from the PDP Platform. As a result of the study, in 2020, 4 companies are at risk, two companies are in the grey zone, seven companies are in the safe zone; in 2021, 5 companies are at risk, eight companies are in the safe zone; in 2022, 2 companies are at risk, four companies are in the grey zone, seven companies are in the safe zone; in 2023, 4 companies are in the grey zone, nine companies are in the safe zone.

Analysis of factors affecting financial distress in Vietnam – an emerging economy in East Asia

Understanding the conditions leading to business failure and predicting them earlier is the best way for companies to overcome and minimize their harm, improve their performance, and avoid financial distress and bankruptcy. This paper aims to measure the level and trends of factors affecting financial distress in Vietnam – an emerging Southeast Asian economy, along with the managerial implications drawn from the research results. Research data were collected from 606 firms listed on the Vietnam Stock Exchange from 2018 to 2022. The Altman Z-score is used to determine the financial distress of these firms. The factors researched and tested in this study are all internal factors divided into two groups with distinct features. Non-financial factors belong to management characteristics; financial factors are typical indicators of a firm’s financial statements. The study uses OLS, FEM, and REM models to analyze the influence of financial factors (Total liability to Total assets, Sales growth, Firm size, and Firm age) and non-financial factors (Board size, CEO duality, Institutional ownership level, Independent member, and Foreign CEOs) on financial distress and GLS regression to overcome the model’s shortcomings. The results show that the factors in the research model significantly impact financial distress, of which six factors (Board size, CEO duality, Institutional ownership level, Foreign CEOs, Sales growth, and Firm age) are negatively correlated. Three other factors (Independent members, Total liability to Total assets, and Firm size) are positively correlated with financial distress. AcknowledgmentThe author thanks everyone who helped make this study possible.

The Effect of Debt Default, Profitability, and Financial Distress on Going Concern Audit Opinion Acceptance

This study aims to analyze the effect of debt default, profitability, and financial distress on going concern audit opinion. The objectt of this research includes the manufacturing company listed on the Indonesia Stock Exchange in 2019 -2022. Independent variables in this research are debt default, profitability. The dependent variabel in this research is going concern audit opinion. This study utilizes samples of 248 secondary data of audited financial reports obtained from 62 manufacturing companies in 2019 – 2022 according to predetermined criteria, selected through purposive sampling. The debt default and profitability are measured by the Current Ratio and Return on Assets ratio, respectively, and financial distress is represented by the Altman Z-score model. The results of the logistic regression analysis exhibit that debt default and financial distress have a significant effect on going concern audit opinion while profitability has no effect on going concern audit opinion.

Ratio Analyses Estimating Financial Risk: Econometric Study Retail Companies the Indonesia Stock Exchange 2020-2023

Aims: This study analyses the effect of activity ratios, sales growth, profitability, and company size on the financial distress of retail companies on the Indonesia Stock Exchange from 2020 to 2023. Study Design: The study uses data on activity ratios, sales growth, profitability, and company size as independent variables and financial distress as the dependent variable. Place and Duration of Study: The research was conducted in retail companies listed on the Indonesia Stock Exchange with a population of retail company subsectors and using purposive sampling criteria. The research data used financial reports published in 2020-2023. Methodology: This type of research is quantitative. The research was tested with SPSS 25 using logistic regression. The total population in the study was 32 types of companies in the retail subsector with several types of companies such as drug retail & distributors, food retail & distributors, supermarkets & convenience stores (minimarkets), department stores, apparel & textile, automotive retail, and electronic retail. Purposive sampling obtained 108 unbalance data for 4 years of observation listed on the Indonesia Stock Exchange in 2020-2023 financial distress was tested with the Altman Z-Score method through logistic regression test. Result: Utilizing logistic regression and the Altman Z-Score, the study reveals that activity ratios and profitability significantly negatively affect financial distress, while sales growth and company size do not have a significant impact. Conclusion: These findings provide valuable insights for stakeholders, emphasizing the importance of optimizing asset efficiency and profitability to mitigate financial distress.

Bankruptcy Prediction Analysis of Life Insurance Companies Using Altman Z-Score dan Ohlson O-Score Methods

Life insurance is one of the non-bank industries that offers guarantees to overcome risks. In its implementation, life insurance companies need to maintain the survival of the company in the midst of increasing economic competition so that they don't face the threat of bankruptcy. Bankruptcy itself is a legal status of a certain entity or company that cannot pay its debts to creditors and the company's operations cannot be continued due to lack of funds. This study aims to compare the accuracy of the Altman Z-Score and Ohlson O-Score methods in predicting the bankruptcy of life insurance companies in Indonesia, such as PT Axa Financial Indonesia, PT Taspen Life, Asuransi Jiwa Bersama (AJB) Bumiputera 1912, PT Avrist Assurance, and PT Reliance Life Insurance Indonesia. The data used in this study is secondary data in the form of financial statements of life insurance companies taken from the official website of the relevant company. The results showed that the comparison between the two models revealed that the Altman method is better in predicting company bankruptcy. This is because the Altman method has a more detailed classification of conditions compared to the Ohlson method.

Financial Distress Analysis of Companies Carrying Out Mass Layoffs Throughout 2023 using the Altman Z-Score Method, Springate Method, and Zmijewski Method

This research aims to estimate the financial condition of four companies that will carry out mass layoffs throughout 2023 using three financial distress prediction methods, namely the Z-Score Altman method, the Springate method and the Zmijewski method. Secondary data was used sourced from the financial reports of each company from 2022 to 2023. The research results showed that financial performance was analyzed using the Z-Score Altman method, the Springate method and the Zmijewski method in four companies, namely PT Net Visi Media Tbk for the period 2022-2023 classified as having the potential to experience bankruptcy, PT GoTo Gojek Tokopedia Tbk for the 2022-2023 period is classified as having a high potential for bankruptcy, PT Bukalapak.com Tbk for the 2022-2023 period is classified as being in good health, and JD.com, Inc. (JD) for the 2022-2023 period is classified as having absolutely no potential for bankruptcy.

Prediksi Kebangkrutan dengan Model Grover, Altaman Z-Score, Springate, Zmijewski, Olhson dan Fulmer: pada Perusahaan Pertambangan di Bursa Efek Indonesia (BEI) Tahun 2017 – 2021

The aim of this research is to determine (1) predictions of bankruptcy for mining companies listed on the Indonesia Stock Exchange using the Altman Z-Score, Springate, Fulmer, Ohlson, Zmijewski and Grover models from 2017 to 2021, and (2) to determine the level of accuracy six models in predicting bankruptcy. The sampling method used purposive sampling with several criteria which resulted in 37 samples of mining companies during 5 years of data observation. The research results show that, using the Altman model and the Ohlson model, it is estimated that 12 companies will experience bankruptcy. Springate model shows that 28 companies will go bankrupt. Meanwhile, the Fulmer and Zmijewski model predicts 31 companies will go bankrupt and the Grover model predicts 2 companies will go bankrupt. The Zmijewski model is a model that has the highest level of accuracy in predicting bankruptcy in mining companies, namely with an accuracy level of 98%.