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To identify predictors of corporate financial distress, using the discriminant and logit models, in an emerging market over a period of economic turbulence and to reveal the comparative predictive and classification accuracies of the models in this different environmental setting.

The research relies on a sample of 27 failed and 27 non‐failed manufacturing firms listed in the Istanbul Stock Exchange over the 1996‐2003 period, which includes a period of high economic growth (1996‐1999) followed by an economic crisis period (2000‐2002). The two well‐known methods, discriminant analysis and logit, are compared on the basis of a better overall fit and a higher percentage of correct classification under changing economic conditions. Furthermore, this research attempts to reveal the changes, if any, in the bankruptcy predictors, from those found in the earlier studies that rested on the data from the developed markets.

The logistic regression model is found to have higher classification power and predictive accuracy, over the four years prior to bankruptcy, than the discriminant model. In this research, the discriminant and logit models identify the same number of significant predictors out of the total variables analyzed, and six of these are common in both. EBITDA/total assets is the most important predictor of financial distress in both models. The logit model identifies operating profit margin and the proportion of trade credit within total claims ratios as the second and third most important predictors, respectively.

This paper reveals the accuracy with which the discriminant and logit models work in an emerging market over a period when firms face high uncertainty and turbulence. This study may be extended to other emerging markets to eliminate the limitation of the small sample size in this study and to further validate the use of these models in the developing countries. This can serve to make the methods important decision tools for managers and investors in these volatile markets.

The purpose of this paper is to explore the non-financial causes of organizational success or failure, provide a better understanding of the symptoms of financial distress and improve the predictive capacity of financial failure models.

The paper utilizes exploratory case studies in investigating the relationship of non-financial factors to organizational success or failure across a sample of sector-specific Australian firms listed on the Australian Stock Exchange. A two-tailed study was designed, in which seven cases from both extremes were chosen from three Australian business sectors: finance, property and manufacturing.

Non-financial factors associated with the organizations studied impacted their success or failure. These factors included management skill, experience and involvement in organizational strategy, feedback and resultant activity, together with board of director composition. The identification of financial and non-financial factors and sound internal processes could be utilized for the development of an early warning predictor of organizational success or failure.

The use of this method is very time-consuming but is highly valuable in case study research, providing a more in-depth understanding of how non-financial factors impact organizational success or failure.

The research will provide a better understanding of the symptoms of financial distress and improve the predictive capacity of financial failure models. The improvement in prediction of organizational failure will reduce the costs of failure to all areas affected, from the large corporation to the small business. The inter-connectivity of all businesses to each other often results in a knock-on effect of failure with the cost being borne by all members of the community in some manner. The level of social impact and cost of failure can only be seen by the enormous costs of the Global Financial Crisis failures.

This paper contributes to the literature on effective qualitative research and explores important areas of consideration for those conducting qualitative multiple-case studies. It is intended to be of use to researchers investigating the area of predictors of organizational failure or success.

This exploratory study examined the qualitative and quantitative financial measures that best describe the patterns, predictors, or degree of success for vision driven organizations. A framework was developed within the methodology to qualitatively partition and link the financial contributions of the organizational and strategic factors within visionary organizations. The qualitative measures were identified utilizing content analysis within the literature stream. Five financial indicators were chosen to represent the respective quantitative measures from 57 visionary organizations over a 16‐year period. The inferential test results from two multiple discriminant analyses and verifying MANOVA tests show the accuracy for predicting the level of a visionary organization at 84 percent. The results of this research suggest that group membership, either visionary or average visionary, can be predicted reliably from a set of financial indicators. This research further suggests that organizations can enhance their opportunities for sustained competitive advantage and supernormal profits by focusing on the alignment of ten core elements of vision driven strategies identified from within the research stream.

The purpose of this paper is to find out whether the financial predictors of failure differ for exporting and non-exporting firms.

The study is based on two samples of French manufacturing micro firms from Amadeus database. Samples of 468 exporting and 1,148 non-exporting firms were divided equally to survived and bankrupted firms. Logistic regression method was used with five financial ratios portraying liquidity, solidity, cash flow sufficiency, profitability and productivity.

The findings suggest that cash flow sufficiency and solidity were important predictors in both firm groups, although the latter was more important in case of exporters. Liquidity was important in case of non-exporters, while profitability in case of exporters. Productivity was not a significant predictor. With these variables, failure of exporters was predicted with a higher accuracy.

This paper contributes to an under-researched area in the failure prediction and international business literature, namely, it outlines whether failure predictors are the same for similar exporting and non-exporting firms. The results indicate that some predictors differ and similar ones can have different importance for exporters and non-exporters.

Although overall well-being is a well-studied phenomenon, financial well-being only recently has attracted scholars’ attention. Accordingly, this study aimed to understand the relationship between financial well-being, its predictors (financial status, financial behaviour, financial knowledge and financial attitudes) and overall well-being.

The authors collected data from 262 working adults.

The results showed that only financial status was positively related to financial well-being and the latter was positively related to overall well-being. It was also found that financial well-being mediated the relationship between financial status and overall well-being. In sum, these results showed a multidisciplinary concept of overall well-being and that individuals tend to prioritize financial security over the other components.

The cross-sectional nature of the data is a limitation.

Practically speaking, this research is relevant because it highlights the evidence of financial status as an important influence on financial well-being, as well as the role of household income in individuals’ financial satisfaction.

The study addresses a call for research on the relationship between financial well-being, its main predictors and how these contribute to explain overall well-being.

The purpose of this study is to explore whether different board diversity attributes (corporate governance aspect) can be used to predict financial distress. This study also aims to identify what type of prediction models are more applicable to capture board diversity along with conventional predictors.

This study used Chinese A-listed companies during 2007–2016. Board diversity dimensions of gender, age, education, expertise and independence are categorized into three broad categories; relation-oriented diversity (age and gender), task-oriented diversity (expertise and education) and structural diversity (independence). The data is divided into test and validation sets. Six statistical and machine learning models that included logistic regression, dynamic hazard, K-nearest neighbor, random forest (RF), bagging and boosting were compared on Type I errors, Type II errors, accuracy and area under the curve.

The results indicate that board diversity attributes can significantly predict the financial distress of firms. Overall, the machine learning models perform better and the best model in terms of Type I error and accuracy is RF.

This study not only highlights symptoms but also causes of financial distress, which are deeply rooted in weak corporate governance. The result of the study can be used in future credit risk assessment by incorporating board diversity attributes. The study has implications for academicians, practitioners and nomination committees.

To the best of the authors’ knowledge, this study is the first to comprehensively investigate how different attributes of diversity can predict financial distress in Chinese firms. Further, this study also explores, which financial distress prediction models can show better predictive power.

The purpose of this paper is to investigate the relationship of financial ratios and non-financial factors of successful and failed corporations in the USA. Specifically, the authors provide evidence on whether financial ratios and non-financial factors can be jointly included as indicators to improve the predictive capacity of organisational success or failure in different countries and sectors.

The paper utilises a mixed method exploratory case study focussing on listed corporations in the US and Australian manufacturing, agriculture, finance and property sectors.

The financial ratio findings demonstrate that (with the exception of the failed Australian manufacturing sector) the integrated multi-measure (IMM) ratio approach consistently provides a higher classification rate for the failed and successful groups than those provided by an individual measure. In all cases the IMM method scored higher for US companies (with the exception of the failed Australian property sector). The findings also show that irrespective of the country location or sector, non-financial factors such as board composition and managements’ involvement in organisational strategy impact on a corporation’s success or failure.

The findings reveal that non-financial factors occur prior to financial ratios when attempting to predict organisational success or failure and the IMM approach enables a more thorough examination of the predictive ability of financial ratios for US and Australian organisations. This intuitively indicates that when combined with financial ratios, non-financial factors may be a useful predictor of corporate success or failure across countries and sectors.

Sound internal processes and the identification of both financial ratios and non-financial factors can be utilised to improve the reliability of financial failure models, enable corrective and preventative steps to be implemented by management and potentially reduce the costs of failure for US and Australian organisations.

This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique.

This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error.

The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress.

This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.

The objective of this paper is to stress the importance of detecting financial frauds in predicting business failures disclosed by the unexpected financial crisis brought by Enron, Worldcom and other corporate distresses involving accounting irregularities.

The most frequently used methodologies in predicting business failures, discriminant analysis and neural network (NN) (based on the Kolmogorov‐Gabor polynomial Volterra series algorithm) are used. This paper suggests a two‐stage NN procedure: the first stage detected the false financial statements, which were excluded from samples that used to predict the business failures at the second stage. The one‐stage discriminant analysis and the NN model are used to contrast the two‐stage approach in terms of accuracy rate.

The one‐stage NN model has a higher accuracy rate in identifying failed firms than the discriminant analysis, while the two‐stage NN approach has an even higher accuracy rate than the one‐stage NN model.

Detecting the fraudulent reporting in advance can effectively improve the accuracy rate of business failure predictions.

The paper draws attention to the importance of excluding fraudulent financial reporting to increase the accuracy rate in predicting business failures.

The purpose of this paper is to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda.

The study employed a cross-sectional research design and data were collected from a total of 400 poor households located in Northern, Eastern, Central and Western Uganda. The authors adopted ordinary least square hierarchical regression analysis to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. The effects were determined by calculating the significant change in coefficient of determination (R²) between the dimensions of social network in explaining financial inclusion. In addition, analysis of variance was also used to test for variation in perceptions of the poor about being financially included.

The findings revealed that the dimensions of ties and interaction significantly explain financial inclusion of the poor in rural Uganda. Contrary to previous studies, the results indicated that interdependence as a dimension of social network is not a significant predictor of financial inclusion of the poor in rural Uganda. Combined together, the dimensions of social network explains about 16.6 percent of the variation in financial inclusion of the poor in rural Uganda.

The study was purely cross-sectional, thus, ignoring longitudinal survey design, which could have investigated certain characteristics of the variable over time. Additionally, although a total sample amounting to 400 poor households was used in the study, the results cannot be generalized since other equally marginalized groups such as the disabled persons, refugees, and immigrants were not included in this study. Furthermore, the study used only the questionnaire to elicit responses from the respondents. The use of interview was ignored during data collection.

Policy makers, managers of financial institutions, and financial inclusion advocates should consider social network dimensions of ties and interaction as conduits for information flow and sharing among the poor including the women and youth about scarce financial resources like loans. Advocacy towards creation of societal network that brings the poor together in strong and weak ties is very important in scaling up access to and use of scarce financial services for improving economic and social well-being.

Contrary to previous studies, this particular study test the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Thus, it methodologically isolates the individual contribution of each of the dimensions of social network in explaining financial inclusion of the poor. The authors found that only ties and interaction are significant predictors of financial inclusion of the poor in rural Uganda. Therefore, the findings suggest that not all dimensions of social network are significant predictors of financial inclusion as opposed to previous empirical findings.