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The main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.

In this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.

The study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.

The findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.

This study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

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.

This study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.

This study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.

The results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .

Although the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.

The proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.

This study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.

To empirically estimate a rough set (RS) model in financial distress prediction for Chinese listed companies and assess its classification accuracy.

RS model is used to test the effect of financial ratios and some non‐financial ratios on the probability of financial distress with a sample of 212 financial distressed firms and 212 healthy firms through years 1998‐2005.

Growth ratio of per share of equity, net return on assets, earnings per share, interest coverage, ownership concentration coefficient, net profit margin, pledge, retained‐earnings ratio and total assets turnover have strong classification power in financial distress prediction of Chinese listed companies, especially the ownership concentration coefficient. Prediction model combining financial and non‐financial ratios outperforms the one just containing financial ratios.

One limitation of this research is that it relies on publicly available data and the RS method. Further research can be devoted to making comparisons between the RS method and other prediction methods, and constructing hybrid prediction models with the use of RS and other artificial intellectual methods as well.

It is necessary to consider every aspect of the company when making financial distress prediction, not just financial ratios, to improve the explanatory power of the prediction model.

This study explores how financial ratios and non‐financial ratios, with the help of RS theory, under the restricted tradability of stocks in the emerging stock market, impact on corporate financial distress. The prediction model employed here considers not only accounting ratios, but also cash flow and corporate governance variables, thus improving the prediction accuracy.

The purpose of this paper is to examine a theoretical base for the financial distress prediction modeling over eight countries for a sample of 2,500 publicly listed non-financial firms for the period from 2000 to 2014.

The prediction model derived through the theory has the potential to produce prediction results that are generalizable over distinct industry and country samples. For this reason, the prediction model is on the earnings components, and it uses two different estimation methods and four sub-samples to examine the validity of the results.

The findings suggest that the theoretical model provides high-level prediction accuracy through its earnings components. The use of a large sample from different industries in distinct countries increases the validity of the prediction results, and contributes to the generalizability of the prediction model in distinct sectors.

The results of the study fulfill the gap and extend the literature through a distress model, which has the theoretical origin enabling the generalization of the prediction results over different samples and estimation methods.

The objective of this paper is to develop a model that can predict financial distress amongst public listed companies in Malaysia using the logistic regression analysis.

The logistic regression analysis used in this paper is geared towards developing a model that can predict financial distress amongst public listed companies in Malaysia.

The results prove that five financial ratios have been found to be significant and useful for corporate failure prediction in Malaysia. The overall predictive accuracy is 91.5 percent and this demonstrates that the logistic regression analysis used is a reliable technique for financial distress prediction. In addition, the predictive accuracy of the model in this paper is higher than that of previous studies, which utilised discriminant analysis rather than the method adopted in this research.

The economic crisis mostly began to affect Malaysia's economic standing in July 1997 causing many companies to fall into financial distress, as they were unable to cope with the unexpected downturn. A financial distress prediction model is therefore required to act as a predictor of Malaysian public listed companies' well‐being prior to a financial crisis and to gauge the warning signals of the onset of a downturn in order to strategize their survival techniques during this phase. This study focuses on public listed companies in Malaysia, thus the model adopted is tailored to suit the given context.

The purpose of this paper is to identify financial characteristics that assess and predict corporate financial distress in publicly traded firms quoted in the London Stock Exchange.

The model incorporates three existing literatures as an alternative to bankruptcy. The model has two stages: the first stage discriminates financially healthy or distressed firms utilizing binary logit regression. The second stage makes use of the univariate analysis. Firms can be further categorized into four possible outcomes: financially healthy, potentially healthy targets and financially distressed and potentially distressed acquisition targets.

It was found that financial distress could be identified as early as three years prior to the event. Moreover, statistically significant differences were found between the four firm sample groups.

The vast changing environment and the financial crisis highlight the need for future research on the world trade implications, as well as the individual macroeconomic variables of each country.

This is the first time a UK study makes use of this model in order to follow the hazard model's procedure based on recent financial data. Due to the scope of the analysis, a new version of the latter procedure is employed. A further innovation that makes the model unique is its ability to classify a firm into one of several a priori groupings according to the latter's individual characteristics. This overcomes the limitation of earlier studies that only considered two possible outcomes for firms.

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.

The purpose of this paper is to investigate the applicability of developed country turnaround predication models as well as an “in country” developed turnaround prediction model for a sample of financially distressed Malaysian companies over the period of 2000‐2007.

Multiple Discriminant Analysis (MDA) technique was used to determine companies' financial health.

It was found that severity of financial distress, profitability, liquidity and size are significant predictor variables in determining turnaround potential of distressed companies in Malaysia. The findings show that developed country turnaround predication models have relatively better prediction accuracies compared to turnaround model based on Malaysian firm‐level data. These models' prediction accuracies were gauged by comparing their predicated successful/failed turnaround companies (Type I and II errors) with actual classification of successful/failed turnaround companies by the Bursa Malaysia, and it was found that developed country models were better than model developed using Malaysian data in identifying correctly some of the actual successful turnaround companies.

The paper's comparisons show that Bursa's methodology is appropriate in classifying and monitoring the distressed companies.

This is believed to be the first paper to examine turnaround of the companies in Malaysian context.