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The purpose of this paper is to develop a model for assessing the audit evidence of the going-concern (GC) assumptions underlying the preparation of financial statements.

This research analyses 678 audit opinions of Italian listed firms from 2007 to 2016 and uses a multiple linear discriminant analysis to create a GC score, which includes variables suggested by the international standards on auditing (ISA) 570 and by literature on GC.

The model provides three cut-off scores which can orient auditors towards issuing the most appropriate GC audit opinions (unmodified opinion, unmodified opinion, which includes emphases of matter, qualified opinion or disclaimer of opinion).

The development of the model is mainly based on public data and does not assess confidential information that is not disclosed in audit opinions.

This model can enable auditors to identify the most appropriate GC opinion and align auditor’s opinions in similar circumstances, thereby reducing their reliance on discretion and increasing the reliability of their judgement with a higher degree of accuracy. Moreover, this research lists additional events or conditions that may individually or collectively cast significant doubt on GC assumptions.

This study goes beyond the traditional decision-making process, apparently binary in nature, between “continuity” and “failure” or between “unmodified” and “modified” opinions. It is conceived to detect the different degrees of uncertainty that affect GC evaluations to orient auditors’ professional judgements.

The purpose of this paper is to discuss and assess the structural characteristics (conceptual utility) of the most popular classification and predictive techniques employed in customer relationship management and customer scoring and to evaluate their classification and predictive precision.

A sample of customers' credit rating and socio‐demographic profiles are employed to evaluate the analytic and classification properties of discriminant analysis, binary logistic regression, artificial neural networks, C5 algorithm, and regression trees employing Chi‐squared Automatic Interaction Detector (CHAID).

With regards to interpretability and the conceptual utility of the parameters generated by the five techniques, logistic regression provides easily interpretable parameters through its logit. The logits can be interpreted in the same way as regression slopes. In addition, the logits can be converted to odds providing a common sense evaluation of the relative importance of each independent variable. Finally, the technique provides robust statistical tests to evaluate the model parameters. Finally, both CHAID and the C5 algorithm provide visual tools (regression tree) and semantic rules (rule set for classification) to facilitate the interpretation of the model parameters. These can be highly desirable properties when the researcher attempts to explain the conceptual and operational foundations of the model.

Most treatments of complex classification procedures have been undertaken idiosyncratically, that is, evaluating only one technique. This paper evaluates and compares the conceptual utility and predictive precision of five different classification techniques on a moderate sample size and provides clear guidelines in technique selection when undertaking customer scoring and classification.

Looks at multiple discriminant analysis (MDA) a technique used to discover differences of the members of one group from another. Stresses that in marketing MDA is better used as a method of identifying the discriminant characteristics between market segments. Says that MDA works by providing maximum separation between the groups and this is obtained by maximising the difference between the means of the groups in relation to the standard deviation within the groups. Posits that many model building problems occurring in MDA are common to other multivariate techniques — especially regression analysis. Concludes that there are a few applications of MDA in marketing which illustrate its exceedingly wide potential wherever classification decisions have to be made.

Prior studies have demonstrated that simple linear discriminant models can be highly successful in identifying financially distressed companies, and therefore useful in predicting corporate failures. Such models have been shown to be both industry and country specific even though their variable selection has been narrow. These models have remained incredibly robust over time despite variations in the definition of the ‘distressed’ state employed for modelling purposes. This paper extends such analysis to the main and second boards of the Kuala Lumpur Stock Exchange (KLSE) in Malaysia, with particular reference to their designation of PN4 companies (those classified as ‘distressed’ in accordance with Practice Note No. 4 introduced in February 2001). The findings of the study show that a single discriminant model has high classificatory power for both boards of the KLSE, and that the optimum model comprises financial ratio variables common to other published models. Previous findings are therefore shown to be substantially generalisable to a new environment and to a different definition of distress.

The purpose of this paper is to perform a comparative study of prediction performances of an artificial neutral network (ANN) model against a linear prediction model like a linear discriminant analysis (LDA) with regards to forecasting corporate credit ratings from financial statement data.

The ANN model used in the study is a fully connected back‐propagation model with three layers of neurons. The paper uses a comparative approach whereby two prediction models – one based on ANN and the other based on LDA are developed using identically partitioned data set.

The study found that the ANN model comprehensively outperformed the LDA model in both training and test partitions of the data set. While the LDA model may have been hindered by omitted variables; this actually lends further credence to the ANN model showing that the latter is more robust in dealing with missing data.

A possible drawback in the model implementation probably lies in the selection of the various accounting ratios. Perhaps future replications of this study should look more carefully at choosing the ratios after duly addressing the problems of collinearity and duplications more rigorously.

The findings of this study imply that since ANN models can better deal with complex data sets and do not require restraining assumptions like linearity and normality, it may be overall a better approach in corporate credit rating forecasts that uses large financial data sets.

This study brings out the effectiveness of non‐linear pattern learning models as compared to linear ones in forecasts of financial solvency. This goes on to further highlight the practical importance of the new breed of computational tools available to techno‐savvy financial analysts and also to the providers of corporate credit.

Alternative payment means have been expanding rapidly in recent years. The need to identify the segments of customers that are targetable for both financial and nonfinancial institutions is growing. The purpose of this paper is to use two different methods, discriminant analysis and decision trees, in order to compare the effectiveness of the two methods for segmentation and identify critical consumer characteristics which determine behavior and preference in relation to the use of payment means.

Using data from 321 bank customers, decision tree and discriminant analysis methods are used, first to test the same set of variables differentiating the customers and then to compare the respective results and prediction ability of the two methods.

Results show that discriminant analysis has a better model fit and segments the customers in a more effective way than the decision tree method. In addition, each method shows different variables to differentiate the customer groups.

The findings are limited to the sector and country of the study, as well as the convenience sample that has been used.

Suggestions for financial managers to better understand their customers’ behavior and target the right group are discussed.

This is the first attempt to compare decision trees and discriminant analysis as alternative segmentation methods for payment means.

The purpose of this paper is to identify the performing and non‐performing companies by using multiple discriminant analysis (MDA) and multiple regression and the ratios that could distinguish between the performing and the under‐performing companies.

First, the study applied the α Jensen technique to classify the Shariah compliance companies into performing and non‐performing. Then, the results from the α Jensen technique with 20 financial ratios are applied to MDA in order to establish models that are used to identify non‐performing and performing companies.

The growth turnover ratio is the only ratio that could discriminate between the performing and non‐performing companies in the plantation industry.

The paper only investigates a sector in the main board of Bursa Malaysia, which is the plantation industry. Future research may look into the whole Shariah counters in Bursa Malaysia.

The paper could assist investors to evaluate and select an optimal investment portfolio.

The paper applies multivariate analysis which does not depend only on one variable. Using the multivariate analysis it provides an alternative to establish models that discriminate between the performing and non‐performing companies. This paper also investigates only the Shariah compliance counters in Bursa Malaysia.

The purpose of this paper is to appraise methodological rigor in the application of discriminant analysis (DA) in export-focused research and to offer guidelines for future studies.

The sample includes 89 empirical peer-reviewed studies, comprising 102 models published over the period 1979-2014. Content analysis and vote counting are used to evaluate each of these studies.

This review highlights major flaws in the application of DA in export research. The shortcomings are self-evident particularly concerning suitability of DA for research context, completeness in the reporting of descriptive results, and validity and reliability of predictive results.

The study takes the position that the lack of methodological rigor may be undermining the eminence of knowledge in exporting, and this has extensive implications for both researchers and practitioners.

This review outlines steps to assess methodological rigor associated with DA and offers guidelines for scholars seeking to enhance rigor in future research.