Academic literature on the topic 'Altman’s Z- score'

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.

This chapter covers modelling of business-credit risk, whether retail or wholesale. (1) Risk 101—i) data sources—variations by firm or loan size (financial statements, traded securities prices, environmental assessments); ii) assessment tools—rating agency grades, business-report scores, public and private firm, hazard, portfolio, and exposure models; iii) rating grades—internal and external (Moody’s, Standard and Poor (S&P), Fitch; S&P provided further insights); iv) small and medium enterprises (SME) lending—including reviewing principals in the personal capacities. (2) Financial-ratio scoring—i) pioneers—including Altman’s Z score and Moody’s commercially successful RiskCalc; ii) predictive ratios—that have appeared; iii) agency usage—for the development of public- and private-firm models; iv) Moody’s RiskCalc—basics and results when first launched; v) non-financial factors—those typically considered, and how objectivity can be improved. (3) Forward-looking data—most provided by human judgment, even the ‘wisdom of the crowd’ inherent in market prices. Rating transitions and functional versus reduced-form models are also used.

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 aims to study the variables that influence, positively or negatively, treasury management and liquidity levels required for the normal working of the analyzed companies. It was concluded that several variables studied showed significant statistical relationships with the companies' liquidity level and types of relationships that matched the initial expectations of the study. Thus, the weight of retained net income plus non-cash expenses in total net assets; the Altman discriminant analysis Z score, representative of the level of companies' financing constraints; the size of total net assets; the weight of current assets minus cash and cash equivalents in total net assets and operating profitability, showed relevant relationships with the liquidity level and can be considered determinants of this variable.

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.

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. Re

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 mo