Search results

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.

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 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.

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.

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 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.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

This chapter aims at examining financial distress issue by designing a comprehensive model to explain and predict financial distress in Egypt. This comprehensive model incorporates accounting ratios, market-based ratios and macroeconomic ratios. The sample of the existing research includes all the listed firms in two main sectors: basic resources and chemicals. Using logistic regression model, the results showed that adding market ratios and macroeconomic ratios enhances the predictability of the model and accounting information are not sufficient to explain financial distress.

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.