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The literature has revealed auditors' going concern risk disclosures are examined in research as a homogenous risk class. This is despite the various going concern modifications auditors are entitled to give pertaining to this issue. A five‐level risk class is established in this paper derived from Australian Auditing Standard pronouncements to examine the appropriateness of auditors' going concern reporting relating specifically to the likelihood of firm failure.

Time is necessary to reveal the appropriateness of going concern reporting therefore a longitudinal research methodology was adopted. The research focuses on all Australian listed companies within the building industry in 1989 and follows all of the reporting of going concern by auditors and directors through until 2007. The building industry was selected because of its volatility, which increased the possibility of going concern reporting allowing a more in‐depth focus in the research. All auditors' going concern modifications were examined along with all indications of going concern problems identified by directors. To properly investigate the appropriateness of auditors' reporting, all sampled audit reports were examined using Altman's Z‐score model which were matched with a risk class model using the relevant requirements to report in order to determine the appropriateness of the auditors' and directors' opinions.

The level of under reporting of going concern risk by auditors (75 per cent) implies they are more affected by the agency relationship found in literature than directors who are found to have an incidence of underreporting of 57 per cent.

Literature classifies auditors along with directors as part of the agency problem. Altman's Z‐score bankruptcy prediction model is used because of its enduring nature, reliability and ability to be externally calculated to independently compare the going concern reporting performance of auditors and directors as part of the contribution to this research area.

The paper for the first time examines going concern reporting at a multi‐risk level rather than the binomial level used in research previously. The approach is developed in this paper using auditing pronouncements. These risk levels are linked with an independent measure being the Altman Z‐score to determine the appropriateness of auditors' and directors' reporting of going concern issues.

The purpose of this paper is to investigate, using data on US manufacturing firms, how and when corporate governance affects managers' decisions to use discretionary accruals and thereby artificially influence company financial reports.

Three‐stage least squares is employed to study the relationship between financial status, corporate governance and financial reporting discretion. The sample spans the years 2001‐2003 during a severe downturn in the US stock market. Financial status is measured with the Altman Z‐score.

A significant difference is found between firms not classified as healthy or failed (i.e. the mid‐range group) and the two extreme categories when examining governance quotient using a well‐known index. A positive relationship is found between discretionary accruals and the governance index. Strong governance appears to reduce the incidence of mid‐range firms engaging in accruals management. The least healthy and the most distressed companies have the weakest relationship with discretionary accruals. By contrast, mid‐range firms are more likely to resort to discretionary accruals.

Non‐executive members of boards of directors are warned to be particularly vigilant about discretionary accruals with firms transitioning between healthy and high‐failure risk.

The relationship between firms' financial health and discretionary accruals reveals an agency problem in credit markets with financially stressed firms. More attention is required on firms whose financial condition is uncertain. Also, it is documented that significant findings of importance to the earnings quality and corporate governance literature by documenting the role of corporate governance on discretionary accruals and financial status.

The main purpose of this study is to examine the relationship between credit risk and financial performance in non-financial firms.

In order to test the relationship between Altman Z-score model as a credit risk proxy and the Return on Asset and Equity as indicator for financial performance with control variables leverage, liquidity and firm size. Least Square Dummy Variable regression analysis is opted. This research's sample included 69 non-financial companies from the Pakistan Stock Exchange KSE-100 Index between 2012 and 2017.

This study establishes the findings that Altman Z-score, leverage and firm size significantly impact the financial performance of the KSE-100 non-financial firms. However, liquidity is found to be insignificant in this study. Altman Z-score and firm size have shown a positive relationship to the financial performance, whereas leverage is inversely related.

This study brings in a new and useful insight into the literature on the relationship between credit risk and financial performance. The results of this study provide investors, businesses and managers related to non-financial firms in the KSE-100 index with significant insight about credit risk's impact on performance.

The evidence of the credit risk and financial performance on samples of non-financial firms has not been studied; mainly it has been limited to the banking sector. This study helps in the evaluation of Altman Z-score's performance in the non-financial firms in KSE-100 index as well.

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.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

The purpose of this paper is to investigate whether proxies considered under ex ante uncertainty hold true under a fixed price mechanism structure. In particular, the study examines whether pre-initial public offering (IPO) financial performance, measured by Altman Z-score, can serve as a proxy for ex ante uncertainty or signalling in an IPO market where a fixed price mechanism is used to determine the offer price.

This study uses solely ex ante information available to prospective investors prior to the IPO to proxy for ex ante variables. It also applies a more sophisticated and robust approach using quantile regression (QR) technique in addition to ordinary least squares (OLS) regression. Applying the QR technique allows the study to produce estimates for the conditional quantiles of the distribution of IPO initial returns and address the violations of basic assumptions of the standard OLS technique.

The results show that for ex ante variables, such as IPORISK, company size, the Altman Z-score measure of pre-IPO performance, audit quality and the technology industry, are significantly related to IPO initial returns. However, the relationship differs across the conditional quantiles of the distribution of IPO initial returns, which would not have been recognised using standard OLS. However, the sign of the coefficients shown by some of these variables contradicts the ex ante uncertainty hypothesis assumption, but they are found to have predictive power in explaining IPO initial returns. These findings reveal unique characteristics of the IPO process and investors in Malaysia. Most importantly, the Altman Z-score is found to be significant in the lower and upper quantiles, but insignificant around the median quantile, which implies that Altman Z-score is important for IPOs with low and high initial returns.

These findings suggest that theoretical explanations of the ex ante uncertainty hypothesis cannot be generalised across financial markets, particularly in the Malaysian IPO market where fixed price offerings are common, and investors are risk averse, whereby they avoid risky IPOs, and prefer to take a small amount of returns against high risks. In addition, the composition of the companies in the market is not as large as the developed markets. This implies that the share price of the IPO may be sensitive to other disclosures in the prospectus, market sentiments or financial news. This study recommends the need for more empirical evidence for this purpose by including other important proxies of ex ante uncertainty, such as the use of IPO proceeds and risk factors that are disclosed in the prospectus to test whether the ex ante uncertainty hypothesis holds true in Malaysia.

This study fulfils the need for finding an appropriate theory that better explains IPO initial returns in the Asian IPO market by focussing exclusively on the pre-IPO information available in the prospectus. It also sheds light on important selected pre-listing information.

The present study aims to determine and benchmark the performance of socially responsible companies (SRCs) in India based on the financial, value-added and combined performance indicators by addressing the climate change problems at the grass-root level.

The present study has used the traditional financial, value-added and combined performance indicators to evaluate and rank the performance of 14 SRCs under the Bombay stock exchange (BSE)-Greenex sustainability index. The technique for order performance by similarity to ideal solution (TOPSIS) and fuzzy analytic hierarchy process (FAHP) algorithms calculate performance scores and assign weights to the indicators from 2015 to 2019. Further, the Altman Z-score methodology has been applied to understand the SRCs propensity toward bankruptcy behavior. The parametric t-test is also performed on the outcomes of TOPSIS scores under different categories of indicators to check the statistical significance.

The performance scores of the TOPSIS algorithm indicate that the financial indicators of SRCs govern the firm performance significantly over the value-added indicators (VAIs). Further, parametric t-test results validate the outcomes of the performance scores by exhibiting that there is no significant difference between the traditional financial and VAIs at a 5% significance level. However, a few SRCs overall performance rankings have improved significantly after including VAIs. Moreover, the Altman Z-score results also reveal that most of the SRCs evaluated in the study are stable, showcasing consistent performance and absent from bankruptcy behavior.

The study has practical implications as follows: (1) to facilitate a clear understanding of investors and portfolio managers in selecting appropriate companies under socially responsible investing (SRI); (2) to provide portfolio diversification insights for domestic and international investors besides advocating the necessity of investing in better performing sustainable companies to safeguard their investments against the future uncertainty and (3) the study results would benefit the regulatory bodies to frame appropriate sustainability policy interventions at the organization level.

In the context of ambiguous inferences on the performance of SRI, no prior study has been conducted to assess the performance of SRCs in the Indian version of sustainability index BSE-Greenex.

Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues.

This paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables.

The results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking.

These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.

This paper is the first comprehensive investigation of the shipping industry's efficiency in five countries from the ASEAN region: Malaysia, Singapore, the Philippines, Thailand and Vietnam.

Employing Data Envelopment Analysis and Stochastic Frontier Analysis, this paper compares efficiency dynamics of 45 international and offshore shipping providers engaged in fishing and ferrying.

The results indicate consistently diminishing efficiency from 2011 to 2017, a phenomenon that persists even in the traditionally efficient companies. Thereafter, this paper develops Altman Z-scores for the sampled companies and notice that despite rising inefficiency, most firms remain unencumbered by bankruptcy concerns, especially those with large capital buffers.

In general, this paper observes a negative relationship between bankruptcy risk and efficiency. Furthermore, the paper notices that reducing inputs does not help boost efficiency.

In terms of novel contributions, this paper is the first (to the best of knowledge) to set a Z-score for the ASEAN-based shipping companies.