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.

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%.

scholarly journals Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1886
Author(s):  
Michal Pavlicko ◽  
Marek Durica ◽  
Jaroslav Mazanec
Keyword(s):  
Prediction Model ◽  
Central Europe ◽  
Financial Distress ◽  
Prediction Models ◽  
Value Added ◽  
Ensemble Model ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction ◽  
Visegrad Group

The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

Predicting corporate financial distress using logistic regression : Malaysian evidence

2006 ◽  
Vol 3 (1) ◽  
pp. 123 ◽  
Author(s):  
You Hoo Tew ◽  
Enylina Nordin
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Financial Distress ◽  
Prediction Models ◽  
Current Model ◽  
Validation Sample ◽  
Policy Makers ◽  
Pooled Data ◽  
Financial Distress Prediction ◽  
Distress Prediction

This study attempts to construct and test financial distress prediction model for Malaysian Companies. The samplefor this study consists of84 companies listed on Bursa Malaysia that became financially distressed in 200/ and 2002 and a matched (by industry and firm size) sample 0/ 84 financially healthy companies. The model is constructed by employing logistic regression analysis based on pooled data of5 years prior tofinancial distress. The model isfirst derived using the estimation sample andthen tested using the validation sample. Adding to the existing research onfinancial distress prediction models, the current model utilizes measures ofshareholders' equity to total liabilities, shareholders' equity to total assets, current liabilities to total assets, total borrowings to total assets andinventory turnover. The results are encouraging, as the model developed/or predicting corporatefinancial distress in Malaysia is reliable up to 5 years prior to financial distress. II is also believed thai the prediction model can be useful to different groups of users such as policy makers, financial institutions, creditors, managers, bankers, investors and shareholders.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

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