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When Jim La Fleur took the helm at GTI, it was a company hovering on the edge of bankruptcy. By using the Altman Bankruptcy Predictor Model in an active way to set strategy, La Fleur was able to return the company to a sound balance sheet. This is a case report of a marriage of an academically developed model and a corporate strategy designed to manage a financial turnaround.

The liquidity component from financial analysis has been found to be an important predictor of a firm's financial well being. Altman's credit worthiness and financial viability system uses, among six other financial measures, the current ratio as a liquidity discriminant variable. The Altman system itself has had considerable success in the prediction of corporate bankruptcy. Altman reported that the system correctly predicted bankruptcy 93% of the time using financial statements one period prior to failure and 87% with financial statements two periods prior. The Value Line Investment Survey also contains a measure of financial strength that incorporates a liquidity measure.

The purpose of this article is to provide commentary on the utility of Altman's Z‐score as a strategic assessment and performance management tool. This possibility is suggested in the recently published book Measuring Organizational Performance – Metrics for Entrepreneurship and Strategic Management Research (Northampton, MA: Edward Elgar, 2006) by Robert B. Carton and Charles W. Hofer.

This paper is a corporate manager's analysis of the utility of Altman's Z‐score as a strategic assessment and performance management tool based on published research, with suggestions for further research.

The analysis supports Carton and Hofer's findings with respect to the utility of the Z‐score as a strategic assessment and performance management tool.

While the Z‐score is both popular and widely used in the fields of credit risk analysis, distressed investing, M&A target analysis, and turnaround management it has received relatively little attention as a strategic assessment and performance management tool. The findings of Carton and Hofer's study, in conjunction with the impressive results achieved by GTI Corporation, suggest that applying the Z‐score in strategy and performance management may also be warranted, especially after more research is undertaken.

This article offers a manager's perspective on new research that indicates the potential of a popular financial distress metric to provide insight in the areas of entrepreneurship and strategic management.

The purpose of this paper is to examine the optimal leverage ratio, speed of adjustment, and which factors contribute to achieving the target of selected telecom companies in a partial adjustment framework from 2008 to 2017. Further is to analyze the likelihood of bankruptcy of sample companies by Altman Z-Score model and to suggest which theory of capitals structure is better in explaining leverage strategies and judicious mix of debt and equity structure of the selected telecom companies.

This paper chooses a partial adjustment model and uses the generalized method of moments technique to identify the variables that influence the target leverage ratio and the factors that influence the speed at which the target leverage is adjusted. Second, the Altman Z-score model is used in this paper to research the financial status of telecom companies using financial instruments and techniques.

For Indian telecom firms, firm-specific variables such as profitability, NDTS and Z-score lead to greater debt adjustment towards optimal level target leverage. The paper also highlights new paradigms in the Indian telecom sector, stating that top market leaders such as Bharti Airtel, BSNL, Idea, Vodafone and R.com, among others, should focus on debt reduction and interest payments, as well as implement new strategies to solve the crisis and change financial policies.

It mainly focuses on firm-specific variables because the firm-specific variables affect the leverage framework. The country-specific variables are not taken into the study. These results may be unique to telecom companies due to some peculiarities existing in the telecom sector in India. Although other sectors, both national and international level, can be taken into consideration.

This paper has ramifications for corporate executives, investors and policymakers in India, for example, in terms of considering different transition costs while changing a telecom company’s financing decisions.

To the best of the authors’ knowledge, this is the first paper of its kind to look at both financial and econometric tools to assess financial performance using the Altman Z-Score model, as well as decide leverage strategies and the pace with which they can be adjusted to target leverage in the context of Indian telecom companies.

Can the Manville Corporation and International Harvester stave off bankruptcy? Both have low Z‐scores in the bankruptcy predictor model, demonstrating danger for Manville, and little chance of survival in its present form for Harvester unless it receives government assistance.

The number of business failures in this country is growing at an alarming rate. While many experts have looked at the specific reasons why particular companies fail, few have examined the general trends and macroeconomic conditions that affect business failures. The author has, and has come to some surprising conclusions.

PT Garuda Indonesia (GIAA) Persero Tbk is the one only pride airline of Indonesian sovereignty. Although the bird achieved abundant international awards and certifications, the bird is dying and needs a remedy immediately. The frequent annual turnover of board executives did not make impact to the financial performance; this seems to be tip of the iceberg, peculiar with the number of restatement over the past decade. Therefore, this paper aims to address the issue through the function of five red flags model which known as Altman Z-score, Sprigate S-score, Grover G-score, Beneish M-score and Dechow F-score.

This is exploratory study of univariate analysis using financial distress and fraudulent financial statement approach, while the type of data is secondary taken from Indonesia Stock Exchange during 12 years observation from 2007 to 2018.

Altman, Springate and Grover produce strong indication of GIAA’s financial distress; all models score the same distress indication by 14 times. All distress models agreed that only 2011 and 2012 classify to the safe zone when GIAA performed the corporate actions. Beneish scores fraud indication by eight times. Dechow scores slightly higher by nine times. The number of fraud predictions in this research are in line with the number of restatement, which proves the assumption that restatement can be used as a signal of the financial statement fraud. When GIAA categorized in safe zone, both Beneish and Dechow score no to fraud, this indicates the fraud occurence during health period is lower.

The motivation behind the financial statement fraud is not discuss through this research but from the primary theory of the fraud triangle. Financial distress possesses strong relationship with pressure factor; therefore, exit from financial crisis is one of the best solution to mitigate the financial statement fraud.

The average of Beneish score is −2,26, slightly above the manipulator threshold which is −2,22. This must be marked as an ample conjecture of GIAA’s fraud inclination and been a highlight for the auditor both internal and external when performing control testing, attestation and other assurance services.

All models in this study can apply to any other corporate issues, especially for evaluating the government company who has loosen the public trust recently in Indonesia such as PT Asuransi Jiwasraya and PT Asabri. Moreover, the pandemic COVID-19 has brought the world to the new unprecedented risk, especially the economic turmoil which lead the possibilities of corporate distress and fraud. By applying these scores, public might have tools as pre-elemenary assessment to serve a decision where to put trust in a company.

This paper reveals a combination from various models of financial distress and financial statement fraud in order to generate the financial solutions named « DDCC » Debt Restructuring, Debt Conversion, Capex Management and Cost Cutting.

The increasing incidence of fraudulent financial reporting by firms in recent years raises concerns about investors' confidence in capital markets. Academicians and industry practitioners adopt diverse risk management techniques to detect fraudulent reporting of financial statements. This paper aims to determine the effectiveness of the Beneish M-score and Altman Z-score models for the early detection of material misstatements at Comscore, Inc., a media analytics firm in the United States of America.

The financial statements of Comscore Inc. from 2012 to 2018 were analyzed with the primary objective of early fraud detection by employing the Beneish M-score and the Altman Z-score.

The study’s outcomes indicate that the Beneish M-score is less predictable in fraud detection compared to the Altman Z-score. The study results did not confirm the efficacy of the Beneish model in predicting fraudulent financial statements. The study concludes that the choice of forensic tool greatly influences fraud detection outcomes.

The research findings can guide the policy decision-making of investors, financial auditors, and forensic auditors as this study provides some evidence of the effectiveness of forensic tools in the detection of financial statement fraud in corporate entities.

This is the first study to apply these two widely used tools to the most recent big corporate scandal: Comscore, Inc.

The objectives of this study are to offer cash based funds flow components as an alternative to financial ratios for classifying the financial performance of companies; to test empirically the ability of funds flow components to distinguish between failed and nonfailed companies with special emphasis on working capital components; to analyse the empirical results and make recommendations for future study.

Outlines previous research on business failure prediction models and investigates the impact of serial correlation and non‐stationarity in financial variables on models based on linear discriminant analysis, logit and cumulative sums using 1974‐1991 data from a sample of failed and non‐failed US firms, plus a similar 1992 sample. Presents and discusses the time series behaviour of the explanatory variables, the estimation of the three types of models and their error rates over time. Concludes that models based on variables with strong positive serial correlation deteriorate over time in their forecasting power; and calls for research to develop stationary models.