Non-financial variables related to governance and financial distress prediction in SMEs–evidence from Egypt

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yasmine M. Ragab ◽  
Mohamed A. Saleh
Keyword(s):  
Financial Distress ◽  
Small Sample Size ◽  
Small Sample ◽  
Content Type ◽  
Financial Variables ◽  
Paired Samples ◽  
Financial Distress Prediction ◽  
Financial Failure ◽  
Distress Prediction ◽  
Lending Decisions

PurposeThis 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.Design/methodology/approachThis 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.FindingsThe 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% .Research limitations/implicationsAlthough 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.Practical implicationsThe 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.Originality/valueThis 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.

Modeling corporate financial distress using financial and non-financial variables

2019 ◽  
Vol 61 (3/4) ◽  
pp. 457-484
Author(s):  
Senthil Arasu Balasubramanian ◽  
Radhakrishna G.S. ◽  
Sridevi P. ◽  
Thamaraiselvan Natarajan
Keyword(s):  
Financial Distress ◽  
Confusion Matrix ◽  
Predictive Ability ◽  
Test Sample ◽  
Business Failure ◽  
Content Type ◽  
Financial Variables ◽  
Institutional Holdings ◽  
Financial Distress Prediction ◽  
Distress Prediction

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

scholarly journals Financial Distress Prediction Using Hybrid Machine Learning Techniques

2020 ◽  
pp. 1-12
Author(s):  
Suduan Chen ◽  
Zong-De Shen
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Financial Distress ◽  
Machine Learning Techniques ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Learning Techniques ◽  
Distress Prediction ◽  
Hybrid Machine ◽  
Financially Distressed

The purpose of this study is to establish an effective financial distress prediction model by applying hybrid machine learning techniques. The sample set is 262 financially distressed companies and 786 non-financially distressed companies, listed on the Taiwan Stock Exchange between 2012 and 2018. This study deploys multiple machine learning techniques. The first step is to screen out important variables with stepwise regression (SR) and the least absolute shrinkage and selection operator (LASSO), followed by the construction of prediction models, as based on classification and regression trees (CART) and random forests (RF). Both financial variables and non-financial variables are incorporated. This study finds that the financial distress prediction model built with CART and variables screened by LASSO has the highest accuracy of 89.74%.

A theoretical approach to financial distress prediction modeling

2017 ◽  
Vol 43 (2) ◽  
pp. 212-230 ◽  
Author(s):  
Ibrahim Onur Oz ◽  
Tezer Yelkenci
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Estimation Methods ◽  
Prediction Modeling ◽  
Content Type ◽  
Financial Firms ◽  
Financial Distress Prediction ◽  
Earnings Components ◽  
Distress Prediction ◽  
High Level

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

scholarly journals Financial distress prediction of Islamic banks using tree-based stochastic techniques

2018 ◽  
Vol 44 (6) ◽  
pp. 759-773 ◽  
Author(s):  
Khaled Halteh ◽  
Kuldeep Kumar ◽  
Adrian Gepp
Keyword(s):  
Financial Distress ◽  
Gradient Boosting ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Z Score ◽  
Data Set ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Stochastic Gradient Boosting ◽  
Distress Prediction

PurposeFinancial 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.Design/methodology/approachThis 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.FindingsThe 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.Originality/valueThese 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.

scholarly journals Financial Distress Prediction of Airlines Companies

2021 ◽  
Vol 5 (2) ◽  
pp. 3-15
Author(s):  
Afiruddin Tapa ◽  
Nurfarah Lyana Ahmad Razif
Keyword(s):  
Middle East ◽  
Financial Distress ◽  
Descriptive Analysis ◽  
Z Score ◽  
Financial Distress Prediction ◽  
Financial Failure ◽  
Significant Difference ◽  
Distress Prediction ◽  
Score Model ◽  
Significant Model

The purpose of this study is to compare three financial failure models: the Altman Z-Score Model, the Springate Model, and the Zmijewski Model, in terms of predicting financial difficulty among airlines in Asia and the Middle East. Based on the results of this study, it is proven by the result of the analysis done for Airlines in Asia and the Middle East that all the three models have predicted that these companies are in financial distress. But, the Altman Z-Score model is the most significant model to forecast financial distress. Although the models employ different ratios in their analyses, this study demonstrates that there is a substantial difference in the analysis of these three models. Another independent T-test demonstrates that the Altman Z-Score Model and the Zmijewski Model, as well as the Springate Model and the Zmijewski Model, have substantial differences. The study employed a descriptive and comparative analysis method, and this model was created to compare the independent variables. The Altman Z-Score model is the most significant model for predicting the financial failure of enterprises, according to the descriptive analysis in this study. While the comparison findings show a large difference between the Altman Z-Score Model and the Zmijewski Model, there is also a significant difference between the Springate Model and the Zmijewski Model. The Altman Z-Score Model and the Springate Model, on the other hand, imply that there is no significant model.

scholarly journals Predicting financial distress using financial and non-financial variables

2015 ◽  
Vol 8 (1) ◽  
pp. 243-260 ◽  
Author(s):  
Francois Van Der Colff ◽  
Frans Vermaak
Keyword(s):  
Financial Distress ◽  
Reasonable Doubt ◽  
Test Results ◽  
Financial Variables ◽  
Model Based ◽  
Financial Distress Prediction ◽  
Model Combining ◽  
Proposed Model ◽  
Distress Prediction ◽  
The Relationship

This study attempts to clarify whether using a hybrid model based on non-financial variables and financial variables is able to provide a more accurate company financial distress prediction model than using a model based on financial variables only. The relationship between the model test results and the De la Rey K-Score for the subject companies is tested, employing Cramer’s V statistical test. A movement towards a Cramer’s V value of one indicates a strengthening relationship, and a movement towards zero is an indication of a weakening relationship. Against this background, further empirical research is proposed to prove that a model combining financial variables with true non-financial variables provides a more accurate company distress prediction than a financial variable-only model. The limited evidence of a strengthening relationship found is insufficient to establish the superiority of the proposed model beyond reasonable doubt.

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