Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932 Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world forecasting model using Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex
Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models
Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results
Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies

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Logit and Probit Application for the Prediction of Bankruptcy in Slovak Companies

Earnings management, business strategy, and bankruptcy risk: evidence from Indonesia.

Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam

With the unprecedented increase in data all over the world, financial sector such as companies and industries try to remain competitive by transforming themselves into data-driven organizations. By analyzing a huge amount of financial data, companies are able to obtain valuable information to determine their strategic plans such as risk control, crisis management, or growth management. However, as the amount of data increase dramatically, traditional data analytic platforms confront with storing, managing, and analyzing difficulties. Emerging Big Data Analytics (BDA) overcome these problems by providing decentralized and distributed processing. In this study, we propose two new models for default prediction. In the first model, called DPModel-1, statistical (logistic regression), and machine learning methods (decision tree, random forest, gradient boosting) are employed to predict company default. Derived from the first model, we propose DPModel-2 based on graph theory. DPModel-2 also comprises new variables obtained from the trading interactions of companies. In both models, grid search optimization and SHapley Additive exPlanations (SHAP) value are utilized in order to determine the best hyperparameters and make the models interpretable, respectively. By leveraging balance sheet, credit, and invoice datasets, default prediction is realized for about one million companies in Turkey between the years 2010–2018. The default rates of companies range between 3%-6% by year. The experimental results are conducted on a BDA platform. According to the DPModel-1 results, the highest AUC score is ensured by random forest with 0.87. In addition, the results are improved for each technique separately by adjusting new variables with graph theory. According to DPModel-2 results, the best AUC score is achieved by random forest with 0.89.

Big data analytics for default prediction using graph theory

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.

Modeling corporate financial distress using financial and non-financial variables: The case of Indian listed companies

This article makes an attempt to empirically examine the relationship between financial distress and earnings management with reference to selected Indian firms. Our sample consists of 150 financially distressed firms during the post-recession period from 2009 to 2014. The present study uses discretionary accruals (DA) as a proxy for earnings management. Multiple regression analysis has been used for this purpose. The study uses cross-sectional modified Jones model to estimate DA, a proxy for earnings management. Altman’s Z-score (Z-score) and distance-to-default (DD) have been used as two alternative measures for financial distress. The study finds that less distressed firms are engaged in higher earnings management. Cash flow coverage (CFC) is found to have a significant negative relationship with earnings management implying that firms with higher CFC have lesser incentive to manage their earnings through DA. The findings are consistent with several prior studies. The findings of the study have importa...

Earnings Management and Financial Distress: Evidence from India:

The paper aims to assess whether a sensitivity variable, industry beta, has a significant impact on the firm's likelihood of default, as an independent predictor variable. The study uses logistic regression and multiple discriminant analysis for matched pair sample of defaulting and non-defaulting listed Indian firms. The industry beta is estimated by regressing the monthly stock return of each individual firm on the monthly return of the respective industry index. The sensitivity variable for industry factors, industry beta, is found to be statistically significant in predicting defaults. Higher sensitivity to industry factors leads to an increased probability of default.

Efficacy of industry factors for corporate default prediction

Purpose – The purpose of this paper is to assess the significance of the Merton distance-to-default (DD) in predicting defaults for a sample of listed Indian firms. Design/methodology/approach – The study uses a matched pair sample of defaulting and non-defaulting listed Indian firms. It employs two alternative statistical techniques, namely, logistic regression and multiple discriminant analysis. Findings – The option-based DD is found to be statistically significant in predicting defaults and has a significantly negative relationship with the probability of default. The DD retains its significance even after the addition of Altman’s Z-score. This further establishes its robustness as a significant predictor of default. Originality/value – The study re-establishes the utility of the Merton model in India using a simplified version of the Merton model that can be easily operationalized by practitioners, reasonably larger sample size and is done in a more recent period covering the post global financial cr...

Predicting financial distress: revisiting the option-based model

Purpose – This paper aims to examine the impact of initial public offering (IPO) grading on earnings management by Indian companies in their IPOs. Specifically, it investigates whether earnings management significantly differs in the pre-IPO grading regime and post-IPO grading regime. Further, it examines whether earnings management significantly differs between high-graded and low-graded IPOs. Design/methodology/approach – The cross-sectional modified Jones model is used to obtain the discretionary accruals, a proxy for earnings management. The impact of IPO grading on earnings management is assessed using multiple regression analysis. Findings – Earnings management is significantly lower in graded IPOs as compared to the ones that are not graded. Further, among the graded IPOs, the high-graded IPOs exhibit lower earnings management as compared to the low-graded IPOs. The findings are robust to the use of an alternative measure for discretionary accruals. Originality/value – IPO grading in India is a uni...

Impact of IPO grading on earnings management

This article is an attempt to explore the usefulness of a model that uses the Piotroski’s F-score and its individual components for predicting the risk of default for a sample of Indian firms. The ...

Khushbu Agrawal

Papers

Earnings Management and Financial Distress: Evidence from India:

Efficacy of industry factors for corporate default prediction

Predicting financial distress: revisiting the option-based model

Impact of IPO grading on earnings management

Default Prediction Using Piotroski’s F-score: