Bankruptcy Prediction Models Research Articles

Diagnosing the Financial Distress in Oil Drilling and Exploration Sector of India through Discriminant Analysis

The present study is mainly devoted to the bankruptcy prediction models and their ability to assess a bankruptcy probability for oil drilling and exploration sector of Indian. The study puts an effort to determine the financial health of 12 selected companies from this sector of India for a period of 5 years. These companies serve the backbone of many other industries such as transport industry, manufacturing industry, automobile industry and so on of the Indian economy. The study has taken the reference of Altman’s Z-score model, where ratios such as working capital to total asset, retained earnings to total asset, earnings before interest and tax to total assets, market value of equity to book value of debt and sales to total assets have been taken. The discriminant analysis is conducted to validate the outcomes of Altman’s model to predict group membership and to forecast the overall industry condition. The study reveals that 75 per cent of the companies are in financially healthy zone. The results indicate that working capital/total assets can very well explain the Z-score. The research on financial health using Altman’s score is very limited in Indian context. Therefore, this study will add value to the existing body of literature for financial risk.

How Significant are the Differences in Financial Data Provided by Key Data Sources? A Comparison of XBRL, Compustat, Yahoo! Finance, and Google Finance

ABSTRACT We compare the financial statement data (excluding footnotes) reported by 105 randomly selected firms in their 10-K filings with data contained in XBRL filings and data reported by three data aggregators/distributors: Compustat, Google Finance, and Yahoo! Finance. We find that 48 percent to 63.2 percent of the 10-K financial statement items available in XBRL filings are not available from the aggregators/distributors. However, aggregator/distributor-provided data contain many financial items that are not in the official 10-K or XBRL filings but could be useful to users. For items included both in XBRL and by aggregators/distributors, all but 0.01 percent of the XBRL data amounts agree with the 10-K filings, whereas 6.5 percent to 7.7 percent of the amounts provided by aggregators/distributors do not, depending on the aggregator/distributor. Most differences are material, and the differences in items used in bankruptcy prediction and earnings quality models result in significant differences in the model results.

An overview of bankruptcy prediction models for corporate firms: A Systematic literature review

Purpose: This paper aims to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between different authors (co-authorship), and to identify the primary models and methods that are used and studied by authors of this area in the past five decades.Design/methodology/approach: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017.Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, demonstrating the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as the researchers with a lot of influence were basically not working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence.Originality/value: We applied the SLR approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this contributes as the link among different elements of the concept studied, and it demonstrates the growing interest in this area.

Eliciting Weights of Significance of Criteria for a Monitoring Model of Performance of SMEs for Successful Insolvency Administrator’s Intervention

Small and medium-sized enterprises (SMEs) are accounted for as a major part of the economy of the EU in terms of part of the population employed, turnover, value-added, etc. Causes of insolvency of SMEs can be different; they are categorized in the paper. A considerable shift from resolving cases of bankruptcy with the sole aim to satisfy creditors’ rights to augmenting and enhancing liquidation and reorganization procedures evolved interest of the authors in creating efficient bankruptcy prediction models and, in particular, methodologies for evaluation and monitoring of the performance of SMEs. In the paper, we reviewed several initiatives and instruments created by the EU for supporting SMEs. The paper laid a foundation for creating a more comprehensive methodology for evaluation of the state of a firm undergoing the process of reorganization. A hierarchy structure of criteria for the evaluation of SMEs was used in the paper; methodologies for eliciting weights of importance of criteria from experts and gauging the level of concordance of opinions of experts were applied. Resulting weights of criteria of performance of an insolvent SME were obtained; the importance of the managerial category of criteria was revealed. Prominent features of hierarchy structures and methodology of using the structure for calculating ultimate weights were described and demonstrated. Gauging concordance of opinions of experts revealed a satisfactory level of concordance of opinions of experts; this allowed to prepare the ultimate weights of criteria for multiple criteria evaluation of SMEs for further research.

Corporate Distress and Bankruptcy Prediction - A Critical Review of Statistical Methods and Models

Bankruptcy prediction has interested and intrigued accountants and researchers alike since early 1930’s. The empirical and theoretical research conducted till date seek to find the best statistical method to develop distress / bankruptcy prediction model and also check the validity of the models developed across different industries, sectors and countries. This paper is an attempt to critically review the various models developed for bankruptcy prediction and the statistical methods adopted in such studies. It is observed that there is no consensus as to the best method and model for corporate distress and bankruptcy prediction. However certain techniques like multivariate analysis. Logistic regression and Artificial Neural Networks have found favour with researchers and academicians alike. Continuous attempts are on to discover newer techniques and methods to develop a robust bankruptcy prediction model.

Analisis Model Altman Z-Score, Springate, dan Zmijewski Sebagai Metode dalam Memprediksi Kondisi Kebangkrutan pada PT. Garuda Indonesia, Tbk Periode 2014-2018

This study aims to find out, describe and explain the results of the application of analysis from the prediction method of bankruptcy of the Altman Z-Score, Springate, and Zmijewski models used in assessing and predicting the potential for bankruptcy with the object of research at PT. Garuda Indonesia (Persero) Tbk 2014-2018. The method used in this research is descriptive research method using a quantitative approach, and the operationalization of the variables used is the independent variable, namely the bankruptcy prediction model with the dependent variable is financial ratios. The data used are secondary data in the form of annual financial reports (annual reports) PT. Garuda Indonesia (Persero) Tbk for the period 2014 to 2018. The results of this study indicate that the Altman Z-Score, Springate and Zmijewski methods are able to measure and predict bankruptcy of companies with different portions according to the calculations applied in each of these analyzes.Keywords: Altman Z-Score, Springate, Zmijewski, and Bankruptcy

Application of Altman Bankruptcy prediction model in Ghana

The study examined the applicability of the Altman Z-score model in predicting bankrupt companies or financially distressed companies on the Ghana Stock Exchange. A sample 10 listed firms were selected and one other company to be used for validation purposes. The validation process involved data for 2016 and 2017 for Aluworks which represented a distressed company and GOIL Ghana Limited which represented non-distressed company. The final analysis was based on a random sample of 10 listed firms using their 2017 financial statement. The results of the initial prediction showed 50 percent of the companies were correctly predicted whiles the others were misclassified. Additional analyses showed that the size of the company influence the probability of bankruptcy whiles the nature of the business does not. The conclusion drawn shows that the Altman Z-score cannot accurately predict financially distressed firms in Ghana but can still be useful in giving signals.

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

Data analytic approach for bankruptcy prediction

Bankruptcy prediction problem has been intensively studied over the past decades. From traditional statistical models to state of the art machine learning models, various predictive models are developed and applied to various datasets. However, models that use machine learning are not used in the field of business, for two main reasons. First, the prediction accuracy does not far exceed the statistical models and second, the results are not interpretable. In this study, we focused on solving the skewness which is a characteristic of financial data. By solving this problem, we obtained 17% average improvement in AUC over existing models. To address the second shortcoming, we analyze the importance of features identified by the XGBoost model. The interpretation of the model differs among categories of data. Our bankruptcy prediction model has high predictive accuracy with clear explanations and is therefore directly applicable to the industry.

Forecasting bankruptcy using biclustering and neural network-based ensembles

Most bankruptcy prediction models that have been analyzed in the literature, and that are estismated using ensemble-based techniques, are still not able to fully embody the true diversity of firm bankruptcy situations. Indeed, these models try to assess all bankruptcy situations either mostly using the same set of variables (bagging, boosting), or using the same set of observations (random subspace). In the first case, an ensemble assumes that any symptom of failure has the same origin. In the second case, it assumes that any financial situation that can lead to failure is the same for all firms. However, there are many situations where these two assumptions do not hold and where a state of bankruptcy may be specific to a given subgroup of firms or may be explained by a particular subset of variables. Certain methods, such as random forest or rotation forest, which combine the characteristics of both random subspace and bagging appear as solutions to this issue. However, they do not always perform significantly better than other ensemble models do. This is why we propose a modeling method that attempts to overcome the limitations of the previous models. It is based on a biclustering technique that seeks out groups of firms that are each characterized by a well-defined subset of variables and on an ensemble technique that is used to embody the full diversity of all bankruptcy situations that belong to each bicluster as precisely as possible. We show how the complementarity between these two techniques can improve forecasts.

Analisis Perbandingan Model Prediksi Kebangkrutan Altman Z-Score dan Zmijewski di BEI Periode 2011 - 2015

This study aimed to compare the use of bankruptcy prediction model Altman Z-Score and Zmijewski on delisting companies on the Stock Exchange the period 2011 - 2015. The study population is a company delisting from the Stock Exchange in the period 2011-2015. The sample consists of 7 companies using method. purposive sampling Secondary data used in the form of financial statements of companies that issued from stock for bankruptcy in the period 2011-2015. The data analysis in this research is to perform the calculation of financial ratios in each sample, according to the variables of bankruptcy prediction model were compared to the model of Altman Z-Score and Zmijewski. Furthermore, the company classifies conditions appropriate point cut-off of each model and did calculations the accuracy of each model. Keywords: Altman Z-Score, Delisting, Bankruptcy, Zmijewski.

Bankruptcy prediction for Japanese corporations using support vector machine, artificial neural network, and multivariate discriminant analysis

This study predicted the bankruptcy risk of companies listed in Japanese stock markets for the entire industry and individual industries using multiple discriminant analysis (MDA), artificial neural network (ANN), and support vector machine (SVM) and compared the methods to determine the best one. The financial statements of the companies listed in the Tokyo Stock Exchange in Japan were used as data. The data of 244 companies that went bankrupt between 1991 and 2015 were used. Additionally, the data of 64,708 companies that did not go bankrupt between 1991 and 2015 (24 years) were used. The data was acquired from the Nikkei NEEDS database. It was found from the results of empirical analysis that the SVM is more accurate than the other models in predicting the bankruptcy risk of companies. In the ANN analysis and MDA, bankruptcy prediction could be made accurately only for some individual industries. In contrast, the SVM could predict the bankruptcy risk of companies almost perfectly for either entire and individual industries. This bankruptcy prediction model can help customers, investors, and financiers prevent losses by focusing on the financial indicators before finalizing transactions.

Bankroto prognozavimo modeliai Europos bankų sektoriui

The global financial crisis has hit the global business and society hard and has caused a mistrust in the monitoring and control of the banking sector a decade ago. The distrust encouraged supervisory authorities to reinforce the bankruptcy preventive measures, that are being developed to prevent or mitigate the negative effects of the new crisis. The development and permanent application of reliable bankruptcy prediction models are the part of such preventive measures. The aim of the study was to create a universal straightforward bankruptcy prediction model for the European banking sector, based on the analysis of financial ratios. The study examines the scholarly literature and deploys the theoretical methods of comparative analysis, critical evaluation, systematisation, generalisation. The empirical research involves expert assessment method, analysis of financial statements, data collection, structuring and modification, modelling: binary logistic regression, correlation, graphical representation of data, ROC curve. Research and results: based on selected 24 variables (ratios), five binary logistic regression models were created. The accuracy was tested on the ROC curve, together with the comparison of II type errors, which were made while predicting bank’s failure 1 year and 2 years before the distress. Main findings were: when models where validated and their II type errors were compared, the conclusion was made that model Z5 is the most suitable for predicting bankruptcy for the European banking sector. The accuracy to identify failing banks 1 year before the distress was 78.57%.
 JEL: F 37, G2, G3

Identyfiacja symptomów ryzyka bankructwa przedsiębiorstwa na podstawie polskich modeli predykcji bankructwa – case study Próchnik S.A.

<span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;">Celem artykułu było wskazanie głównych symptomów ryzyka bankructwa spółki Próchnik, wobec<span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"> której w 2018 r. ogłoszono upadłość likwidacyjną. Analizę i ocenę zagrożenia niewypłacalnością przeprowadzono na podstawie polskich modeli predykcji bankructwa. Badaniem objęto działalność przedsiębiorstwa<span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"> w latach 2010<span style="font-family: TimesNewRomanPS-BoldMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"><strong>–</strong><span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;">2017. Wśród najważniejszych zidentyfiowanych czynników ryzyka, które przesądziły o niewypłacalności spółki Próchnik, wskazano utratę rentowności, problemy z płynnością fiansową, malejącą<span style="font-family: TimesNewRomanPSMT; font-size: 9pt; color: #231f20; font-style: normal; font-variant: normal;"> efektywność zarządzania zasobami majątkowymi przedsiębiorstwa oraz niewłaściwą strukturę kapitałową.</span></span></span></span></span></span>

A Machine Learning-Based Peer Selection Method with Financial Ratios

SYNOPSIS Researchers and practitioners have used industry classification systems (e.g., SIC) to select peer firms and create an evaluation benchmark. However, we argue that the choice of peer firms should depend on the research goals. A single peer selection method is not adequate in all circumstances. This study provides a novel approach that yields flexible groupings of firms using clustering techniques. We select the set of financial ratios related to a particular research objective and apply K-medians clustering to identify peer firms. In the subsequent year, a new variable is constructed to capture firms' deviation from peer firms. Significant deviations between a firm and its peers may indicate potential anomalies. We evaluate the usefulness of this K-medians clustering-based peer selection approach by incorporating this variable into a misstatement detection model and a bankruptcy prediction model and find that information about the clustering-based peers can enhance the performance of existing models.

부도예측 모형에서 뉴스 분류를 통한 효과적인 감성분석에 관한 연구

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data...

Bankruptcy Prediction Model of Banks in Indonesia Based on Capital Adequacy Ratio

Objective - The purpose of this study is to examine the influence of capital on bankruptcy banks. The hypothesis of this research is that capital has an effect on the bankruptcy of a bank. Methodology/Technique - This research examines financial reports between 2005-2014. An econometric model with a logistical regression analysis technique is used. In this study, capital is measured by CAR, taking into account credit risk; CAR by taking into account market risk; Ratio of Obligation to Provide Minimum Capital for Credit Risk and Operational Risk; Ratio of Minimum Capital Adequacy Ratio for Credit Risk, Operational Risk and Market Risk; Capital Adequacy Requirements (CAR). Findings - The results show that the capital adequacy ratio for market ratio and capital adequacy ratio for credit ratio and operational ratio support the research hypothesis and can form a logit model. The test results of CAR by taking into account credit risk, Minimum Capital Requirement Ratio for Credit Risk, Operational Risk and Market Risk and Minimum Capital Provision Obligations do not support the research hypothesis. Novelty – This paper contribute to bank bankruptcy prediction models based on time dimension and bank groups using financial ratios which are expected can influence bank in bankrupt condition. Type of Paper - Empirical. Keywords: Banking crisis, Cost of bankruptcy, Adequacy Ratio, Financial ratios, Prediction models JEL Classification: G32, G33, G39. DOI: https://doi.org/10.35609/jfbr.2019.4.1(2)

Analisis Model X-Score Zmijewski dalam Memprediksi Financial Distress Pada Industri Barang Konsumsi yang Terdaftar Di Bursa Efek Indonesia

This Study aimed to test the X-Score Model Analysis Zmijewski In Predicting Financial Distress In Manufacturing Company Sector Consumer Goods Industry Listed on the Indonesia Stock Exchange. The population in this study are a Manufacturing Sector Consumer Goods Industry listed in Indonesia Stock Exchange during the period 2011 to 2015 as many as 37 companies. The sample is determined by purposive sampling with criteria for companies listed on the Indonesia Stock Exchange and companies that publish the complete annual financial statements the period 2011 to 2015 as many as 17 companies. Data used is secondary data obtained from www.idx.co.id. The analysis technique used was analysis using bankruptcy prediction model that is Zmijewski X-Score. So its results if its value (<0) or is negative then the company is in good financial condition or no bankruptcy, the Z-smallest value of -4.1519, and the value of Z-Top of -1.2751 and value Average -rata of -2.4763.

Personal bankruptcy prediction using decision tree model

Purpose Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007, and the total accumulated personal bankruptcy cases stood at 131,282 in 2014. This is indeed an alarming issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy, as well as on the society. From the aspect of individual’s personal economy, bankruptcy minimizes their chances of securing a job. Apart from that, their account will be frozen, lost control on their assets and properties and not allowed to start any business nor be a part of any company’s management. Bankrupts also will be denied from any loan application, restricted from travelling overseas and cannot act as a guarantor. This paper aims to investigate this problem by developing the personal bankruptcy prediction model using the decision tree technique. Design/methodology/approach In this paper, bankrupt is defined as terminated members who failed to settle their loans. The sample comprised of 24,546 cases with 17 per cent settled cases and 83 per cent terminated cases. The data included a dependent variable, i.e. bankruptcy status (Y = 1(bankrupt), Y = 0 (non-bankrupt)) and 12 predictors. SAS Enterprise Miner 14.1 software was used to develop the decision tree model. Findings Upon completion, this study succeeds to come out with the profiles of bankrupts, reliable personal bankruptcy scoring model and significant variables of personal bankruptcy. Practical implications This decision tree model is possible for patent and income generation. Financial institutions are able to use this model for potential borrowers to predict their tendency toward personal bankruptcy. Social implications Create awareness to society on significant variables of personal bankruptcy so that they can avoid being a bankrupt. Originality/value This decision tree model is able to facilitate and assist financial institutions in evaluating and assessing their potential borrower. It helps to identify potential defaulting borrowers. It also can assist financial institutions in implementing the right strategies to avoid defaulting borrowers.

Predicting corporate bankruptcy: What matters?

Whether accountingor market-based information should be employed to predict corporate default is a long-standing debate in finance research. Incorporating a regime-switching mechanism, we establish a hybrid bankruptcy prediction model with non-uniform loadings in both accounting- and market-based approaches to reexamine the issue. We find the following. Creditors should increase the loading on market-based information when large and liquid corporations are considered. Conversely, for companies with incremental information involved in accounting reporting proxied by discretionary accruals, banks could emphasize accounting ratio-based variables more than they are already emphasized. Since managerial discretion in accounting numbers could serve as a tool to bring undisclosed information about the firm to the public, the weight on accounting-based information could be increased for firms with high information asymmetry. In addition, the loading on market-based (accounting-based) information should be increased (decreased) during periods of financial crisis, defined by negative gross domestic product growth.