Academic literature on the topic 'Bankruptcy; Altman's Z-Score Model'

Financial failure has various hidden reasons in corporate firms' life cycles, and finding the symptoms of financial sickness is becoming crucial in worldwide economies. There are many studies and models used in the literature conducted on different variables, sectors, and economies. The most common methods and models used in the bankruptcy area come out to be the logistic regression (LOGIT) and neural network. Altman model is preferably used in most of the bankruptcy prediction studies conducted with different financial indicators. Among important indicators are solvency, profitability, and liquidity. Multiple discriminant analysis (MDA) model and MDA and Z-score and support vector machine are used as alternatives. Artificial intelligence and machine learning models are becoming popular. Literature shows that Altman model is preferably used in most of the bankruptcy prediction studies.

The study aimed to build a risk metric for finding the lower boundary limits for Altman’s z-score bankruptcy model. The new metric included a volatility of Altman’s variables and predicted the riskiness of a firm bankrupting in adverse situations. The research examined whether the new risk metric is feasible and whether it provides satisfying outcomes compared to Altman’s z-score values during the same period. The methods to conduct the analysis were based on Value at Risk methodology. The main tools used in constructing the model were Monte Carlo simulation, Lehmer random number generator, normal and t-distribution, matrices and Cholesky decomposition. The sample firms were selected from FTSE 250 index. The important variables used in the analysis were all Altman’s z-score variables, and the period under observation was 2001–2007. The selected risk horizon was the first quarter of 2008. The first results were promising and showed that the model does work to the specified extent. The research demonstrated that Altman’s z-score does not provide a full and accurate overview. Therefore, the lower bound risk metric developed in this research, produces valuable supplementary information for a well-informed decision making. To verify the model, it must be back- and forward tested, neither of which was carried out in this research. Furthermore, the research elaborated on limitations and suggested further improvement options for the model.

The aim of this study is to investigate the effects of firm bankruptcy risk and auditing factors on the market value of firms and to investigate the effect of auditing on firm bankruptcy risk. The period of the research is 2011-2021. In the study on BIST Sustainability-25 Index, in the first model, the dependent variable is the market value/book value; the independent variables are Springate S Score, Fulmer H Score, Altman Z Score, Çolakoğlu MFA Score; the ratio of the number of independent auditor members to the total number of members; the number of members in the supervisory board; and the number of early risk detection committee members.

The study of the financial imbalances of companies is a common topic for academics and practitioners because bankruptcy affects financial stability and modifies the investors' behavior. Since the 1960s, financial ratios have been used as diagnostic tools and also as independent variables within models aimed at quantifying firms' financial risk (e.g., Altman's Z-Score). In parallel, the strategic theory has developed theoretical constructs to explain why competitiveness is empirically heterogeneous. The resource-based view argues that companies can outperform rivals if they manage scarce, expensive, and hard-to-imitate resources. Ultimately, outperformers should be able to avoid (or overcome) financial imbalances. This chapter intends to analyze whether IT resources modify firm performance and financial risk. To do that, the authors collected data from a random sample of Galician SMEs, combining questionnaires, focused interviews, and public financial data. Hypotheses are explored by applying parametric statistical methods.

This paper delves into the evolving landscape of Islamic finance in Türkiye, where Participation Banks (PBs) adhere to Islamic law and ethical principles, setting them apart from Conventional Banks (CBs). The unique nature of PBs demands tailored assessments of their financial health. The primary objective is to evaluate the financial stability and credit risk of Türkiye's PBs through a comparative analysis with CBs. The study employs the Non-Performing Loans (NPL) ratio in conjunction with the Emerging Market (EM) Score model—a modified version of Altman's Z-Score which is widely used in predicting the bankruptcy of firms including banks. The combination provides a comprehensive evaluation and a deeper understanding of financial stability.
 Focused on six major PBs—Kuveyt Türk, Albaraka Türk, Türkiye Finans, Ziraat, Vakıf, and Türkiye Emlak—the methodology entails collecting and analyzing financial data from official sources, including the Participant Banks Association of Turkey (TKBB) and the Banking Regulation and Supervision Agency (BRSA).
 Anticipated outcomes include enhanced decision-making and the development of robust risk management strategies for Turkish PBs, reinforcing their financial stability. The comparative analysis with CBs aims to unveil competitive advantages and unique challenges, offering valuable insights for policymakers, regulators, and stakeholders in the Turkish banking sector.

The problem of bankruptcy prevention is growing in importance under conditions of the decline of economic growth and quarantine restrictions caused by the COVID-19 pandemic, which has significantly affected the domestic economy. In the second reading, the Ukrainian Parliament adopted amendments to the Code of Ukraine on the Bankruptcy Procedure, which banned moratorium on bankruptcy initiation by the creditors. Thus, there was approved “Draft Law on Amendments to Certain Legislative Acts to Regulate Certain Issues of Bankruptcy Procedures for the Period of Implementation of the Measures Aimed at Preventing the Emergence and Spread of the COVID-19 Pandemic” No 4220. This moratorium was introduced in the framework of measures for business support due to the COVID-19 pandemic. Quarantine restrictions caused by the COVID-19 pandemic have affected many businesses throughout the world. First of all, this is due to strict quarantine measures imposed by the governments of many countries: closure of shopping and entertainment centers, a ban on all public events, restrictions on the movement both within the country and when crossing its borders, reduction of production (due to the establishment of limits for the simultaneous stay of workers in one room), etc. Quarantine has ruined consumer sentiment and almost halted several industries including retail, hotel and restaurant business, air travel. The amount of budget revenues has decreased. As a result of quarantine, Ukrainian companies have frozen investments and production chains, and some of them are on the verge of bankruptcy. The main economic sign of bankruptcy is reduced to a single point. It is inability of the enterprise to meet the requirements of creditors. However, in order to avoid numerous bankruptcies on insignificant debts, the minimum amount of debt is determined, at which a bankruptcy case can be initiated. Macroeconomic efficiency of the institution of bankruptcy directly depends on the systemic nature of the relevant fragment of the national legislation, availability of the detailed representative economic statistics and the level of conceptual development of effective anti-crisis regulation. At the level of microeconomics, bankruptcy means not just stopping the local production process, i.e. the loss of a sustainable source of permanent income and social security. And at the level of macroeconomics there is the opposite situation; bankruptcy means rehabilitation of production from inefficient forms of its organization and inefficient management, overcoming cyclical recession and modernization of the technological base of production. A modern approach to the study of bankruptcy is associated with the definition of objective economic signs of corporate bankruptcy and specific signs of financial insolvency of the enterprise, assessment of the effectiveness of basic legal procedures for bankruptcy (supervision, external management, bankruptcy proceedings, and amicable settlement). Financial preconditions for insolvency and bankruptcy of the enterprise are analyzed in accordance with Methodical recommendations on detection of signs of insolvency of the enterprise and signs of concealment of bankruptcy, fictitious bankruptcy or bringing to bankruptcy; Methodology of in-depth analysis of the financial and economic condition of insolvent enterprises and organizations. Financial statements are the sources of information for analysis and detection of signs of bankruptcy. To predict the risk of bankruptcy, it is necessary to be guided by regulatory sources, data of accounting, statistical, operational accounting and reporting. Necessary information can also be obtained from documentary inspections, audits, orders, directives, economic and legal materials (contracts). To study the results of financial and economic activities of the object of study there can be used accounting data, which contains extensive analytical information. According to primary documents, it is possible to establish the causes of overspending, payment of fines, perpetrators, determine the legality and appropriateness of business transactions. The main sign of bankruptcy is inability of the company to comply with creditors’ claims within three months from the date of payment. After this period, creditors have the right to apply to the arbitral tribunal to declare the debtor company a bankrupt. Bankruptcy is the result of interaction of internal and external factors. Due to the limitations of the COVID-19 pandemic, 1/3 of the business destruction is associated with internal factors and 2/3 with external factors. Bankruptcy characterizes realization of catastrophic risks of the enterprise in the course of its financial activity, as a result of which it is unable to meet the requirements set by creditors and meet obligations to the budget. Among a wide range of methods used to determine the characteristics of various phenomena and processes, to identify the features of development, to study the dynamics of changes at the enterprises under conditions of the threat and development of crisis, there can be distinguished the main ones: expert (expert assessments); research and statistical; analytical; method of analogues. The whole set of methods for assessing the state of the enterprise is based on three main approaches, which include: the use of a system of indicators and informal indicators (criteria and features); setting the maximum number of indicators in different areas of the enterprise; creation of a separate system of integrated indicators. In the practice of analysis and assessment of the enterprise state the most common approach is the one that involves the use of a system of indicators and informal indicators. Integrated factor models developed using multidimensional multiplicative analysis are often used to assess the probability of bankruptcy and the level of creditworthiness of the enterprise. Bankruptcy forecasting methods based on the use of financial ratios are as follows: Two- and five-factor models for estimating the probability of bankruptcy based on Altman’s “Z-score”; Model of Roman Lis, W. Beaver; Method of rating assessment of financial condition (rating number); R – bankruptcy risk prediction; Taffler’s prediction model; Fulmer’s model; Springgate model; Generalized model developed on the basis of discriminant function; PAS-ratio. Integrated factor models of E. Altman, Lis, Taffler, Tishau and others are often used to assess the probability of bankruptcy and the level of creditworthiness of the enterprise (Table 1), developed using multidimensional multiplicative analysis.

This case study focuses on a major challenge faced by Yes Bank as a result of increasing NPAs, a 30-day moratorium imposed by the RBI on its operations, and withdrawal limits of Rs. 50,000 from their accounts. This situation downgraded the value of the bank and built a bad image among its customers. Yes Bank was the UPI companion for almost 20 apps, according to the National Payments Corporation of India (NPCI). However, as a result of its failure, Yes Bank has suspended most of its UPI accounts. Now the government is trying to fix the problem and looking into what went wrong at Yes Bank. Altman's Z-Score, developed by American professor Edward Altman in 1968, is a numerical measurement that can be used to predict the chances of bankruptcy, and after calculating the z-score, it is possible to conclude that Yes Bank is still safe from going bankrupt.

To protect investment and ensure repayment of payables, recent studies have focused on identifying the relationships between company bankruptcy and internal fraud. The P-score model that is based on the most popular Altman Z-score model has been developed to indicate the manipulation of financial statements. Purpose of the study is to determinate the accuracy and the feasibility of P-score and Z-score models to detect fraudulent bankruptcy in regional conditions, based on reports of the Latvian construction companies that failed due to fraud, and during the verification of other known data. Research methodology is based on the background studies of P-score testifying, applying this approach to the Latvian condition. The present study analyzes the behaviour of the two models in identifying distress and fraud. To testify the results of the study, the authors use the financial analysis methods, comparison, statistical and quantitative research methods. Findings have shown the possibility of using the P-score and Z-score technique for bankruptcy fraud detection at the Latvian companies, based on the construction sector samples. The accuracy of the method is above 80%. Research limitations – acquisition a large amount of data on companies that are in the process of analytical studies on the recognition of their insolvency and having signs of fraud is not possible due to the confidentiality of information. Practical implications – the results of the study may be applicable to the audit of the company, investment reliability assessment, partnership evaluation and economic examination to detect fraud. Originality/Value of the study is the first test of practical implication of P-score model in Latvia and the Baltic countries on the samples of small and medium-sized construction companies. The authors propose improving the coefficients of the P-score model taking into account the requirements for financial statements in Latvia

The phenomena of accounting manipulations and bankruptcy likelihood have always been a topic of interest among researchers. The key objective of the current study is to examine the impact of fraudulent accounting practices on the likelihood of bankruptcy, and the performance of firms. Beneish M-score model and Jones model have been applied to evaluate earnings quality, whereas the Altman Z-score model has been used to analyze the level of financial distress. Based on the analysis of secondary data collected from 33 Nordic banks for the period 2011–2018, the findings disclose that Z-score of most of the sample banks has been found to be relatively high thus representing their high level of financial health. The study does not rule out potential earnings management measures applied by the sample banks. Furthermore, earnings manipulations increase the bankruptcy likelihood, especially in case of larger banks. The financial data manipulation practices artificially enhance the financial performance of banks, however, in a broad perspective; such manipulations can trigger potential financial distress

"A large number of studies on bankruptcy prediction are published every year. The topic of SME failure prediction has evolved over the past decades into a relevant research area that has grown exponentially across many disciplines, including finance and management, for obvious reasons. This has been motivated by the massive toll on SMEs caused by the global crisis of 2007-2009, the recent COVID-19 crisis and the resulting need to update indicators of SME failure. Many authors during the last fifty years have examined several possibilities to predict business failure. They have studied bankruptcy prediction models under different perspectives but still could not indicate the most reliable model. This paper focuses on the Czech economy, specifically at small and medium-sized enterprises (SMEs). This article aims to find if there exist different factors that could predict bankruptcy for manufacturing and commercial companies. Considering the research objective, the following hypotheses were set: H1: Indicators used in the financial distress model for manufacturing companies differ from commercial companies.; H2: Applying a model based on different segmentation criteria improves the reliability of bankruptcy prediction. It is the ongoing research about the value of several popular bankruptcy models that are often applied, namely the Altman Z-score, the Ohlson O-score, the Zmijewski's model, the Taffler's model, and the IN05 model. The logistic regression has been used to investigate around 1800 companies, of which 308 failed during 2010 – 2017. Reached results confirm both hypotheses and some suggestions arise from it. When we develop a bankruptcy model, it is necessary to sort companies according to different criteria. It also confirms findings of the last years literature review the closer the similarity of businesses, the greater accuracy of bankruptcy models. Further, it is required to exploit common used financial indicators with a combination of modified indicators to assess the probability of bankruptcy precisely."

This paper analyses the financial health of firms that are members of selected cluster organizations operating in the Czech and Slovak Republics. The research includes two research samples, which are member firms of cluster organizations IT Cluster and Košice IT Valley. Both of the above-mentioned cluster organizations were established as a result of a cluster initiative and associated entities from the ITC sector. The firms that form the cores of the above-mentioned cluster organizations are mostly active in the sectors with the following statistical classification: NACE 620100, 620200, and 620900. The main objective of the present research is to analyze selected financial health indicators of member firms of both cluster organizations and to determine whether or not there are significant differences in the development of selected financial indicators between individual firms. To achieve the objective, the profitability ratios and Altman's Z´´score model were used. The research results are discussed in the conclusion of the paper.