Z-score Model Research Articles

ANALYSIS OF COMPARISON OF BANKRUPTCY PREDICTION ABILITY BETWEEN ALTMAN Z-SCORE ANALYSIS AND ZMIJEWSKI X-SCORE ANALYSIS IN FINANCIAL SERVICES COMPANIES LISTED ON THE INDONESIA STOCK EXCHANGE FOR THE 2017-2021 PERIOD

This study aims to determine the comparative analysis of bankruptcy predictive ability between the Altman z-score and Zmijewski x-score analyses of financial services companies listed on the Indonesia Stock Exchange for the 2017-2021 period. This study uses secondary data in the form of financial statements of each financial service company, where the sample used in this study is 41 financial service companies listed on the Indonesia Stock Exchange. The data analysis method used is the Altman z-score and Zmijewski x-score models with the help of Microsoft Excel 2019 software. The results of the study reveal that (1) from the results of the Altman Z-Score, it is obtained that as many as 19 companies are predicted to experience bankruptcy within five years of the study period, while 6 financial services companies are predicted not to go bankrupt during the five years of the study period. (2) Zmijewski's analysis shows that 3 financial service companies are predicted to go bankrupt during the five-year study period, and 29 financial service companies are predicted not to go bankrupt during the five-year study period. (3) The results of calculating the level of accuracy found that the Altman Z-Score model is the best in predicting the bankruptcy of financial services companies with an accuracy rate of 68.78%, while the Zmijewski model only obtains an accuracy rate of 16.09%.

BANKRUPTCY PREDICTION WITH THE ALTMAN Z-SCORE, SPRINGATE, ZMIJEWSKI, AND GROVER MODELS

Purpose – This study aims to predict bankruptcy towards food and beverage subsector and finding the most accurate analysis model among the Altman Z-Score, Springate, Zmijewski, and Grover
 Design/methodology/approach – This analysis uses quantitative descriptive method using Ms. Excel, then performed Kruskal Wallis and accuracy test using IBM-SPSS
 Findings – The results by three-year average calculation using Altman Z-Score model shows that 2 out of 21 companies have the potential to go bankrupt, Springate model has 9 out of 21 companies, Zmijewski model has 3 out of 21 companies, and based on Grover model there are 2 out of 21 companies. The results of the hypothesis test showed that there were differences between the models and the Zmijewski model is the most accurate for food and beverage subsector companies.
 Originality – This study uses the most accurate analysis model among the Altman Z-Score, Springate, Zmijewski, and Grover by using 2017-2019 financial report data published on Indonesia Stock Exchange (IDX)
 Keywords: Bankruptcy, Altman Z-Score, Springate, Zmijewski, Grover
 Paper Type Research Result

Evaluation of the influence of the COVID-19 pandemic on the financial stability of airlines with different business models

The Russian airline market became the first in the world to record the positive revenue growth per passenger-kilometer in 2021 after a sharp decline in passenger traffic a year earlier. This indicates its gradual recovery after the biggest crisis in the history of aviation. The purpose of the article is to assess the financial stability of the Russian airlines over the past three years: 2019 (pre-COVID-19), 2020 (COVID-19) and 2021 (post-COVID-19) based on the applying of the three bankruptcy likelihood prediction models (Altman’s Z-score model for emerging markets, the AIRSCORE model of Chow et al., and the P-model of Pilarski and Dinh). The analysis was conducted for the 4 airlines - Aeroflot, Pobeda, Smartavia and NordStar - each of them having a different business model that they follow (national flag carrier, low-cost carrier, ultra-low-cost carrier and regional carrier, respectively). It should be noted that during the study period there was the rapid growth of low-cost carriers, even during the COVID-19 crisis. Traditional (full-service) airlines, on the contrary, had negative dynamics both in terms of operational and financial indicators. Calculations of bankruptcy models showed that low-cost airlines were more financially stable during the COVID-19 pandemic, while full-service air carriers experienced uncertainty during 2020 and only gradually recovered in 2021.

A comparative analysis of Altman's Z-score and T. Jury's cash-based credit risk models with the application to the production company and the data for the years 2016-2022

Research Questions: In the present paper the author answers the following research questions: 1) What are the potential strengths of the credit risk model based on the cash flow principle? 2) What are the weaknesses of the accrual-based credit risk model? 3) What are the benefits of the combined use of both cash-based and accrual-based credit risk modeling methods when analyzing companies? Motivation: nowadays there are no researches comparing the accrual-based credit risk model to a cash-based credit risk model with the application to a production company trading its shares on Stock Exchange. However, for investors, auditors and financial institutions it is important to know if there is a difference between these two models in the interpretation of analysis results, and determination of prebankruptcy stage of the company and credit risk default. Idea: in this paper, the author has focused on the comparative analysis of the cash-based credit risk model and the accrual-based credit risk model. The author applies it to the case of a manufacturing company and compares the effectiveness of determining the probability of default using a cash-based credit risk model and an accrual-based credit risk model. Data: the data analysed is obtained from the annual reports, managerial reports and auditor’s reports of Linas Agro Group for the years 2016-2022. The company information is taken from Nasdaq Baltic where Linas Agro Group has its shares traded. Tools: mixed research methods were used, combining quantitative calculations with analysis based on qualitative information. The author elaborates on the cash-based credit risk model based on the improved Timothy Jury’s template. The accrual-based model chosen for comparison and analysis is Altman’s Z-Score model. Findings: The results of the study have shown that the cash-based model is more effective in determining credit risk and default probability. The cash-based model indicated a high-risk default for the manufacturing company in four years out of seven years, while Altman’s Z-Score showed the company to be in the moderate risk grey zone in five years out of seven, and the two last years the model indicated the company in the green zone. The author suggests to financial institutions, financial managers, and investors using a cash-based credit risk model or combination of it with the accrual-based model. Contribution: the paper contributes to the knowledge about the comparison of cash-based and accrual-based credit risk models and emphasizes their strengths and weaknesses. It helps investors, auditors, business owners, and finance professionals to make a decision about which credit risk model to use for the analysis to determine the pre-bankruptcy state of the company, avoid bad loans and improve investment decision-making. It also encourages the academic society for further research and comparison on the topics of accrual-based and cash-based credit risk models in the strive to develop the ultimate credit risk model capable to analyse the data as precisely as possible.

Analisis Perbandingan Potensi Financial Distress Dengan Metode Altman Z-Score Dan Springate Pada PT. Kediri Tani Sejahtera Sebelum Dan Saat Pandemi Covid-19

This research aims to analyze the application of the Altman Z-Score method and the Springate method to predict financial distress at PT Kediri Tani Sejahtera and aims to analyze the comparison of financial distress from the Altman Z-Score and Springate methods before and during the Covid-19 pandemic for the 2018-2022 period. The type of this research is quantitative descriptive research, using the Altman Z-Score and Springate methods. The results of the research show that the Springate model is a reliable model for anticipating possible financial difficulties in businesses based on the two models that have been studied, while the Altman Z-Score model shows that corporations are in a gray state or experienced some minor financial difficulties during 2018-2022.

When two Z-scores meet—analysis of exercise capacity of children and adolescents with Kawasaki disease by a new Z-score model of coronary artery and a new Z-score evaluating peak oxygen consumption

BackgroundCoronary artery (CA) Z-score system is widely used to define CA aneurysm (CAA). Children and adolescents after acute stage of Kawasaki disease (KD-CA) have a higher risk of developing CAAs if their CA Z-score ≥ 2.5. Z-score system of peak oxygen consumption (Peak VO2 Z-score) allows comparisons across ages and sex, regardless of body size and puberty. We aimed to compare the exercise capacity (EC) indicated by peak VO2 Z-score during cardiopulmonary exercise testing (CPET) directly between KD-CA with different CA Z-score.MethodsKD-CA after acute stage who received CPET in the last 5 years were retrospectively recruited. CA Z-score was based on Lambda-Mu-Sigma method. Max-Z was the maximum CA Z-score of different CAs. KD children with Max-Z < 2.5 and ≥ 2.5 were defined as KD-1 and KD-2 groups, respectively. Peak VO2 Z-score was calculated using the equation established based on Hong Kong Chinese children and adolescent database.ResultsOne hundred two KD-CA were recruited (mean age: 11.71 ± 2.57 years). The mean percent of measured peak VO2 to predicted value (peak PD%) was 90.11 ± 13.33. All basic characteristics and baseline pulmonary function indices were comparable between KD-1 (n = 87) and KD-2 (n = 15). KD-1 had significantly higher peak VO2 Z-score (p = .025), peak PD% (p = .008), peak metabolic equivalent (p = .027), and peak rate pressure product (p = .036) than KD-2.ConclusionsKD-CA had slightly reduced EC than healthy peers. KD-CA with Max-Z ≥ 2.5 had significantly lower peak EC than those < 2.5. Max-Z is potentially useful follow-up indicator after acute stage of KD.

MERGERS AND ACQUISITIONS. A CREDIT RISK EVALUATION IN THE GREEK BANKING INDUSTRY SHORT- AND LONG-TERM TIME PERIOD AFTER THE EVENT

&lt;p&gt;The Greek banking system was greatly affected by the financial crisis that began to plague Greece in 2010. Therefore, in order to have competitive Greek banks, a rally of acquisitions and mergers of Greek banks began in 2010 and ended mainly in 2015. So, in essence, the Greek economy was left with only four large Greek systemic banks, which took over the other large and small distressed Greek banks under the specter of bankruptcy. This wave of acquisitions and mergers in the Greek banking system created strength in Greek banks mainly in terms of assets and at the same time protected the interests of Greek depositors. This paper deals with the assessment and calculation of the credit risk that led to the bankruptcy of the four Greek systemically important banks with Z-Score models. These banks are Piraeus Bank, National Bank, Eurobank, and Alpha Bank. The time period for conducting our study is limited from 2015 to 2020. The year 2015 is essentially the last year in which all the important and large mergers and acquisitions of Greek systemic banks that occurred immediately after the onset of the financial crisis in Greece in the year 2010, were completed. The last year of our study is the year 2020 when the pandemic started in Greece. In our paper, extensive reference is made to the credit risk models that we apply in our study, namely the Z-Score, the Z-Score for banks, and the Zeta model. The analysis of our findings is accompanied by tables, diagrams, and comments on them. The final comparative results obtained are forecasts for imminent possible or non-bankruptcy of the Greek systemic banks we examined. At the end of our paper, we present some conclusions about the Greek banking system and its future.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JEL:&lt;/strong&gt; G21, G33, G34&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0021/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

MERGERS AND ACQUISITIONS. AN EVALUATION ANALYSIS OF BANKRUPTCY PRE AND POST OF BOTH EVENTS. EVIDENCE FROM GREECE

The banking sector is one of the most important sectors in economy of the world and occupies a prominent position in the development. The banking system in Greece is regulated by the European Central Bank (ECB) and is composed of various types of banks, including systemic commercial banks, non systemic commercial banks and cooperative banks. The purpose of this study is to study the case of the reduction or not of the occurrence of bank bankruptcy after the acquisition they made of banks with financial difficulties. For this purpose, we study the possible bankruptcy of the acquired banks before their acquisition, as well as the possible bankruptcy of the acquiring banks before and after the acquisition. There are several models for predicting bankruptcy, including financial ratio analysis Z-score model, Zeta model, financial soundness model and other models. In this study to predict bankruptcy for banks we use the Zeta model for banks. This model is one of the commonly used in the international scientific community. The time period of the study is defined after the beginning of the global financial crisis in the year of 2008 and before the pandemic. This global financial crisis as known invaded Greece mainly in year 2010 an the pandemic started at the end of year 2019. The examined time period concerns both a short-term and a long-term period of time for the most rational drawing of conclusions. This specific study is based on the analysis of two of the most characteristic cases involving Greek banks involved in takeovers. One case concerns Greek cooperative banks and the other one concerns Greek systemic banks.&#x0D; Keywords: Cooperative Banks, Mergers &amp; Acquisitions, Cash Flows Ratio, Profitability, Insolvency.&#x0D; JEL Classifications: G21, G33, G34

Analisis Prediksi Financial distress Menggunakan Model Altman Z-Score dan Springate pada Perusahaan Sub Sektor Pertambangan Batubara yang Terdaftar di BEI 2017-2022

This research was conducted by analyzing and comparing the level of accuracy for predicting bankruptcy (financial distress) through the Altman Z-Score and Springate models for companies. This study uses a data source for the annual financial statements of coal mining sub-sector companies listed on the IDX for the 2017-2022 period as secondary data. The research sample using purposive sampling was obtained by 11 samples. Testing the data using quantitative analysis techniques using the SPSS 26 program. The results of the research on the Altman Z-Score model found 3 companies with bankruptcy conditions and 2 companies with gray zone conditions. Meanwhile, in the Springate model, 4 companies went bankrupt. The results of the research test are that there is a significant difference in predicting bankruptcy conditions using the two prediction models. The accuracy level of the Springate model has a greater percentage than the Altman Z-Score model. Both models produce the same type I error percentage rate, while the type II error percentage rate in the Springate model is higher than the Altman Z-Score model. So, every company needs to predict bankruptcy every year and can be used as an early warning of the company's financial condition.

Financial Reporting Fraud and Models to Assist in Detecting Financial Statement Fraud

The article has used the methodology, analysis and synthesis to accomplish the set research objectives. Accordingly, based on the methodology, the article has presented the concept, purpose and importance of information provided in financial statements. Under many influences such as pressure to commit fraud, opportunities for fraud or management's attitude will lead to the intention to manipulate the financial statements. Next, the article also analyzes typical fraud cases, the impact of fraudulent financial statements on all financial market participants and identifies common forms of financial fraud in Vietnam. Vietnam. From these common types of fraud, the authors also propose two typical models to support fraud detection that are highly appreciated, the M-score model and the Z-score model.

Financial Distress Risk Levels of Listed Small and Medium Enterprises in the Philippines

Small and Medium Enterprises (SME) play a significant role in the Philippine economy in terms of promoting entrepreneurship and technological innovation, encouraging local development and alleviate poverty levels. However, SMEs are more susceptible to financial constraints and difficulties attributable to limited access to capital and weak financial management practices, among others, which lead to financial distress and insolvency in the long run. Anchored on the Early Bankruptcy Theory, a company’s financial distress can be at-tributed to its inability to meet its obligations which stem from poor management, low profitability, inadequate cash flow, or excessive leverage, among others. Hence, the Study aimed at describing and determining the financial dis-tress risk levels of sample SMEs in order to provide valuable insights into the financial health and stability of these companies. Specifically, the Study ascertained the nature, determinants and levels of financial distress risks using the Altman Z-Score Model (1968). Based on the result, sample companies at SAFE levels were deemed to have strong financial positions with good profitability, liquidity, leverage, and solvency while sample companies at DISTRESS levels could experience financial difficulties with lack of liquidity, declining revenues, and reliance on debt financing from third parties. Future research can (1) expand the determination of financial distress risk levels into non-listed SMEs and other listed companies from key sectors using a longer time frame to assess changes in the financial distress risk levels resulting from changes in economic conditions and (2) investigate the impact of financial ratios on financial distress risk while also considering corporate governance measures affecting it.

DEVELOPMENT OF PROPERTY AND REAL ESTATE COMPANIES: Z-SCORE METHOD APPROACH

This research aims to evaluate the development of public companies in the property and real estate sector from 2017 to 2020. The method used to assess such development is the Z-score model. The Z-score model classifies the company into three conditions: safe, gray, and distress. The study showed that of the 40 companies studied, most companies, above 72.5%, fall into the safe category. Companies that fall into the gray area category are second, and companies in the distress category are third. From 2017 to 2020, companies that fall into the safe category tend to decline. On the other hand, companies that fall into distress tend to increase.

DEVELOPMENT OF PROPERTY AND REAL ESTATE COMPANIES: Z-SCORE METHOD APPROACH

This research aims to evaluate the development of public companies in the property and real estate sector from 2017 to 2020. The method used to assess such development is the Z-score model. The Z-score model classifies the company into three conditions: safe, gray, and distress. The study showed that of the 40 companies studied, most companies, above 72.5%, fall into the safe category. Companies that fall into the gray area category are second, and companies in the distress category are third. From 2017 to 2020, companies that fall into the safe category tend to decline. On the other hand, companies that fall into distress tend to increase.

THE EFFECT OF FINANCIAL CONDITIONS AND COMPANY SIZE ON THE GOING CONCERN AUDIT OPINION

This study aims to analyze the effect of financial condition and company size on going concern audit opinion. For the measurement proxy, the independent variable of the study, namely the company's financial condition, is measured by the Revised Altman Z-Score Model, while for company size it is measured by the natural logarithm of total assets. As for the dependent variable of the study, namely the going concern audit opinion measured by a dummy variable based on the financial statements presented by the company in accordance with the disclosure of a going concern audit opinion. The number of observations in this study were 245 observations taken from 49 companies in the mining sector with five years of observation. The sampling technique used was the purposive sampling method by taking samples from S&amp;P Capital IQ for financial data for 2017-2021 which were then processed using STATA ver. 17. The result is empirical evidence that financial conditions have a negative effect on going-concern audit opinions, while company size has a positive effect on going-concern audit opinions. For the control variable, only auditor affiliation has an effect on going concern audit opinion with a negative relationship.

Using the Altman Z-Score Model to Forecast the Financial Distress of a Subset of NIFTY 50 Companies in the Indian Stock Market

The financial stability of a company has a significant impact on the growth of a country and the well-being of society. Financially sound companies safeguard stakeholders' funds and utilize them for the benefit of society and the nation. This study aims to assess the financial distress status of selected NIFTY 50 companies in the Indian Stock Market. Out of the 50 NIFTY 50 companies, only those outside the finance industry were chosen for analysis. The researcher utilized the financial statements of these companies as secondary data for the period 2022-23. This research paper is descriptive and analytical in nature. The Altman Z-Score model was used by the researcher to measure the financial distress of the companies, revealing that out of the 39 selected companies, 9 are in a state of bankruptcy.

THE CORRELATION IN CORPORATE DEFAULTS: EVIDENCE FROM NON-FINANCIAL LISTED COMPANIES IN SRI LANKA

Default correlation is the extent to which one company's default causes another's default. It measures the relationship between a firm’s individual default probability and the joint default probability among firms. This study makes theoretical contributions by examining the degree of default correlation among non-financial listed firms in terms of credit quality, time horizon, systematic risk, firm size, and liquidity. The researcher selected 550 firm-year observations (240 for default and 310 for non-default) consisting of 55 non-financial listed companies in Sri Lanka from 2012 to 2021. The default firms are selected using the three criteria during the selection period: suffering from losses, having negative net worth, or suffering from negative operating cash flows for more than three consecutive years. The non-default firms are selected by a couple with the default firms based on the same industry and the highest market capitalization. Altman’s (1968) Z-score model is used to determine the credit ratings as high, medium, and low. The study follows Lucas’s (1995) proposed standard binomial approach to measure the default correlation. The study revealed that time horizon, poor credit ratings, high beta, and small size increase the default correlation in the Sri Lankan context; however, liquidity is not a good measurement for default correlation analysis in Sri Lankan context. Credit risk management could underestimate the portfolio credit risk without considering the default correlation. The dynamic nature of default correlation shows that an increment in default risk of individual credit assets engages with a disproportionate increment in portfolio credit assets.

Effect of Liquidity and Profitability on The Level of Financial Distress Using the Altman Z-Score Model

This research aimed to examine the impact of the current ratio and return on assets on the degree of financial distress in the automotive and component manufacturing sectors of the Indonesia Stock Exchange during the period of 2015–2020, utilizing the Altman Z-score model. Furthermore, the objective of this investigation is to examine the primary determinant between the current ratio and return on assets about the degree of financial distress, utilizing the Altman z-score model, within the automotive and component sub-sector manufacturing firms that are publicly traded in the Indonesia Stock Exchange from 2015 to 2020. The present study employs a descriptive-verification research methodology. The present study employs secondary data from information sourced from https://idx.bei.co.id/. Conduct data collection methodologies utilizing documentation techniques. The present study has selected a population of 13 manufacturing companies in the automotive and component sub-sectors listed on the Indonesia Stock Exchange from 2015 to 2020. Non-Probability Sampling was utilized to determine the sample size of ten companies that meet the established criteria. The data underwent analysis through the utilization of multiple linear regression as well as hypothesis testing via the t-test and F-test. The findings of the present investigation suggest that the current ratio and the return on assets exhibit a noteworthy and favorable impact on the degree of financial distress, as measured by the Altman z-score, at a rate of 73.4%.

Application of the Altman Z-Score Model in Assessing the Financial Health of Garment Manufacturing Enterprises Listed on the Vietnam Stock Market

The article has provided the content and function of the Z-score model in predicting the possibility of bankruptcy through the actual financial situation of the enterprise. The study uses two research methods, namely the analytical method and the meta-analytic method. Through the methodology, the team obtained an overview of the study and identified gaps in which to conduct the research. Next, the research team aelected a sample of 9 garment manufacturing companies listed on the Vietnamese stock market. Combining the collection of secondary data from financial statements and the theory of the Z-score model, we have a basis to evaluate the financial situation of these 9 enterprises in 2022. 4 enterprises having in good financial condition, 4 enterprises with difficult financial situations and 1 enterprise with financial distress consistent with the actual situation of all 9 enterprises. The analysis results also show that the Z-score model is reliable and quite effective in assessing financial health and predicting the bankruptcy of enterprises.

Perbandingan Prediksi Kebangkrutan Menggunakan Metode Grover, Metode Altman Z-Score dan Metode Springate

This study aims to analyze the bankruptcy rate of companies using the Grover model, Altman Z-score model, and Springate model. This type of quantitative research with the population uses companies that conducted IPOs in 2021. The sampling technique used is purposive sampling and the number of samples obtained based on predetermined criteria is 54 company samples. The results of this study showed that sample companies that predicted bankruptcy using the Grover method had an accuracy rate of 83%, and an error rate of 17%. The Altman Z-score method has an accuracy rate of 87%, an error rate of 9%, and a gray area rate of 4%. The Springate method has the highest accuracy rate of 94%, and an error rate of 6%. This bankruptcy prediction analysis can help company owners to immediately anticipate the factors that cause bankruptcy and for investors can be used as information in making decisions to invest in shares in IPO companies in 2021.

Predicting Financial Stability of Banks in Nigeria Using the Altman Z Score Model.

The global economy has brought with it a lot of complexity to the financial service industry (Banking, Insurance, and FinTech’s). With recent global events like the pandemic, the need to forestall and mitigate any corporate business failure has become necessary. The financial sector is seen as a very important aspect in the growth and stability of any economy. The stability of this sector of the economy has been of utmost concern to regulatory agencies and relevant stakeholders. Altman Z score is a financial tool for dissecting the stability of entities with published financial statement. This research paper aim to predict the strengths and weaknesses in selected commercial banks in Nigeria using the Altman Z Score model. To achieve this, we look at past records of banks for a period of twelve years to foretell what the future holds and what stakeholders should expect. We look the extent to which the model is been used by the regulatory agency for bank failure particularly after the bank consolidation in the banking sector. Ten (10) banks were selected for this study using purposeful sampling technique. The study concluded that Z-score model is an effective tool for predicting distress in financial institutions and was therefore recommended.