Bankruptcy Prediction Research Articles

Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking

Bankruptcy is an impact caused by a company's financial failure. Financial failure in the company must be avoided so as not to cause losses to the company. In the research that was carried out utilizing a data set from the Taiwan Economic Journal as many as 6,819 to be trained using machine learning algorithms using classification techniques. The goal obtained from the research conducted is to obtain a classification technique with the best accuracy results. The method used in this research is preprocessing using the synthetic minority over-sampling technique to handling unbalanced data sets. Then, the results of the balanced data set will be processed using a genetic algorithm-support vector machine feature selection algorithm to reduce the attributes of the data set. Data sets that have experienced reduced attributes will be trained using the stacking method with a single classifier base learner in the form of k-nearest neighbors, naïve bayes, decision trees with classification and regression tree models, gradient boosting decision trees, and light gradient boosting. The meta-learner used in the stacking method is extreme gradient boosting. The results of the accuracy obtained from the research conducted were 99.22%.

Prediction of Potential Bankruptcy of Mining and Petroleum Companies in Indonesia

This research was conducted to provide information to stakeholders about the sustainability of mining and oil companies in Indonesia. Thus, the financial distress model (Altman Z-score) was adopted in this study to predict bankruptcy potential in mining and oil companies in Indonesia. This research uses descriptive method with a quantitative approach. The population in this study consists of mining and oil companies listed on the Indonesia Stock Exchange (IDX) in the 2010-2018 period. The sampling technique used purposive sampling technique. The results of the data calculation show that working capital to total assets (X1), retained earnings to total assets (X2), earnings before interest and taxes to total assets (X3), market value of equity to book value of debt (X4), and sales to total assets (X5) has a positive value. This means that all mining and petroleum companies in the sample have a very healthy financial condition and indicate that the company is in the non- bankrupt company category. This study only analyses financial statements (fundamental analysis), thus ignoring the cost of capital. This will make it difficult to know whether a company has succeeded in creating value or not.

Using textual analysis in bankruptcy prediction: Evidence from Indian firms under IBC

Identifying and managing credit risk is vital for all lending institutions. Historically, credit risk is assessed using financial data from published financial statements. However, research indicates that the ability to detect financial hardship may be improved by textual analysis of firms’ disclosed records. This study aims to establish an association between themes and words from Management Discussion and Analysis (MDA) reports of firms and corporate failures. The study took a sample of 57 Indian listed firms declared bankrupt under the Insolvency and Bankruptcy Code (IBC) along with a matched sample of 55 solvent firms (matched by industry and size) for the period of FY2011–2019. The first part of analysis identifies negative words from the published reports and compares them with the negative words of the Loughran-McDonald dictionary. Then a thematic analysis is done to identify the key themes from the MDA reports and the significant themes are validated with their corresponding financial ratios in the third step using a panel logistic regression. Word analysis results show that IBC firms have significantly greater negative tone (2.21 percent) as against 1.30 percent of solvent firms. Thematic analysis results show that manageability, activity and performance are significant themes for predicting financial distress. Financial variables such as ownership pattern, promoters’ shares pledged, return on capital employed, asset utilization are some of the ratios in sync with the key themes. The study recommends that lenders and other stakeholders should look beyond financial statements which may be ‘window dressed’ by firms to qualitative disclosures in annual reports which may forewarn against impending financial distress. Acknowledgments The infrastructural support provided by FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

Springate bankruptcy prediction model of the commercial sector of veterinary products in Ecuador

Los cambios no lineales y abruptos del entorno hacen que la esperanza de vida de las empresas esté limitada debido a los riesgos ligados a sus operaciones. El objetivo de la investigación es aplicar el Modelo de fragilidad empresarial Springate en compañías del sector comercial de productos veterinarios para la determinación de su exposición al riesgo de insolvencia. Así mismo, la presente investigación tiene un enfoque cuantitativo caracterizado por el uso de la estadística discriminante múltiple y la correlación de Pearson. El alcance de este artículo es correlacional porque mide la relación de las variables de liquidez, endeudamiento, rentabilidad y rotación, utilizadas por el modelo de predicción de quiebra y por su diseño es de tipo longitudinal debido a que evalúa el desempeño financiero de las empresas en diferentes períodos de tiempo, durante los años 2015 al 2019. Los resultados de la aplicación del Modelo de predicción de quiebra muestran que la mayor parte de empresas están expuestas al riesgo de bancarrota por factores como: problemas de liquidez, baja rentabilidad que no cubre el costo de capital y poca rotación. URL: https://revistas.uta.edu.ec/erevista/index.php/bcoyu/article/view/2081

BANKRUPTCY PREDICTION ON FIRM PERFORMANCE IN PRE – DURING COVID -19 PANDEMIC: SPECIAL REFERENCE TO LISTED MAINBOARD HOTEL COMPANIES IN SRI LANKA

The influence of corporate bankruptcy on the economy is considerable as it encompasses shareholders, financial lenders, operational lenders, and the government. It is necessary to do a bankruptcy evaluation so the businesses can receive early bankruptcy warning signs. The earlier signs of insolvency are identified, the better for management because they can take immediate action to correct the issue. This paper aims to investigate the impact of bankruptcy prediction on firm performance with involving COVID -19 pandemic with a focus on listed mainboard-hotel companies in Sri Lanka while referring current Sri Lanka’s economic crisis. When there is a financial crisis, it is crucial to choose a model for bankruptcy prediction. The study uses semi-annual secondary data to examine a sample of 12 mainboard-hotel companies listed on the Colombo Stock Exchange from year 2016 to 2022. Altman's and Kida Z-scores are the bankruptcy prediction models used to measure bankruptcy. According to the findings, there are seven safe zone hotel companies, three hotel companies are in grey zone and two are recognized as distressed. Return on equity, return on assets and employee productivity were used to construct independent variables. The study also discovered that the profitability and liquidity ratios could foretell the insolvency of mainboard-hotel companies listed on the Colombo Stock Exchange. The findings of the study examined the comparison between model estimations of the study and the actual status of the firms. The study showed the Altman's and Kida Z-scores classification models have 89.6% and 69.5% accuracy for the average predictive ability of business discontinuation, respectively. Overall Altman’s Z-score is better than the Kida model as most of the hypotheses have been proved and prediction rates are in a good position. The variables have a statistically significant association between bankruptcy risk. Therefore, all objectives have been achieved in the study.

Effect of Profitability Ratio on Bankruptcy Prediction of Dairy Cooperative Societies in Meru County, Kenya

Many sectors of the economy across the globe are concerned with the avoidance of bankruptcy in order to operate as going concerns. The purpose of this study was to analyse the influence of financial analysis on bankruptcy prediction for dairy cooperative societies in Meru County, Kenya. The objective of the study was to determine the influence of profitability ratio on the bankruptcy prediction of dairy cooperative societies in Meru County, Kenya. The hypothesis was derived from the objective of the study. The research was anchored on value maximisation theory. Descriptive and correlational research designs were used. The target population of the study was 13 dairy cooperative societies in Meru County that were in operation and had audited financial statements for the period of study (2017-2021). A census survey of all 13 dairy cooperative societies was conducted. A checklist was used to collect the secondary data, which was analysed using descriptive statistics and a bivariate model. The significance of the predictor variable on bankruptcy prediction was tested using the t-statistic, and the overall significance of the model was tested using the f-statistic at a 5% level of significance. The research findings were presented in the form of tables and graphs. The study established a statistically significant positive relationship between profitability ratio and bankruptcy prediction (coefficients=12.415, p=0.017<0.05) at a 5% level of significance. The management of dairy cooperative societies is encouraged to closely monitor the financial indicators of profitability. The findings of this study would be important to policymakers, such as the management of dairy cooperative societies and farmers who are key stakeholders in dairy cooperative societies. Scholars and researchers would find this study being of great interest since the gap for further research has been provided

The Influence of Institutional and Managerial Ownership on Financial Distress with Total Debt to Asset Ratio as a Moderating Variable

Literature regarding bankruptcy prediction illustrates that the variables that are often used as predictors of bankruptcy can be grouped into four. These four things are financial ratios (including company and market characteristics), macroeconomic variables, corporate governance, and other special variables. The aim of this research is to determine whether there is an influence of institutional ownership on financial distress, managerial ownership on financial distress, the total debt to asset ratio mediates the influence of institutional ownership on financial distress, the total debt to asset ratio mediates the influence of managerial ownership on financial distress. The object of this research is property and real estate companies listed on the Indonesia Stock Exchange (BEI) between 2017 and 2021. The sample consists of 32 companies. The analysis used is multiple linear analysis using the partial least squares program. The results show that institutional ownership has no significant effect on financial distress, managerial ownership has no significant effect on financial distress, total debt to asset ratio does not mediate the effect of institutional ownership on financial distress, total debt to asset ratio does not mediate the effect of managerial ownership on financial distress.

Analisis Prediksi Kebangkrutan Menggunakan Metode Altman Z-Score Pada PT. Dua Sahabat Sejati Borneo

The aim of this research is to analyze bankruptcy prediction using the Altman Z-Score method at PT. DSSB for the 2019-2021 period. This type of research is descriptive quantitative. Using secondary data with the research population of the financial statements of PT. DSSB period 2019–2021. The research sample is the balance sheet financial statements and profit/loss reports for the 2019–2021 period. Data analysis used analysis of financial performance ratios and bankruptcy prediction analysis Altman Z-Score (Modified Altman). The results show that from the calculation of financial performance, the liquidity ratio is below the industry standard due to an increase in trade payables in each period. The activity ratio on the company's total turnover ratio can maximize the use of assets, the working capital turnover ratio shows a negative number because the working capital owned is not enough to finance sales activities. The profitability ratio is below the industry standard due to the high cost of goods sold and high general administrative costs, as well as other expenses. In calculating the Z-Score analysis, it is always in a dangerous zone, which is a condition where a company experiences financial difficulties with high risk.
 Keywords: Financial Performance, Bankruptcy Prediction, Altman Z-Score

Analisis Prediksi Kebangkrutan Dengan Menggunakan Metode Altman Z-Score (Studi kasus pada PT. Acset Indonusa Tbk dan PT. Nusa Kontruksi Enjiniring Tbk)

This study aims to determine the bankruptcy prediction of PT Acset Indonusa tbk and PT Nusa Kontruksi Enjiniring tbk in 2021 using the Altman Z-Score method. The data source used is secondary data, namely in the form of Published Financial Reports of Companies issued by the Indonesia Stock Exchange. This study uses a quantitative descriptive method. The technique used is the Altman Z-Score bankruptcy prediction model with the calculation results that the two companies are in unfavorable results or a dangerous zone so that the two companies are predicted to experience bankruptcy.

Prediksi Kebangkrutan Menggunakan Metode Altman dan Springate pada Perusahaan Penerbangan yang Terdaftar di Bursa Efek Indonesia

This study aims to predict bankruptcy in airline companies listed on the Indonesia Stock Exchange, using the Altman and Springate methods during the 2019-2021 period. This research uses quantitative methods in the form of secondary data. The data used is in the form of financial statement data for airline companies listed on the IDX through the website www.idx.com. The population used are airline companies, namely, PT Garuda Indonesia Tbk, PT Air Asia Indonesia Tbk, PT Indonesia Transport & Infrastructure Tbk, and PT Jaya Trishindo Tbk. The results of the Altman and Springate methods can categorize companies as bankrupt or not bankrupt. This study has limited time in collecting research data. Based on calculations using the Altman formula, PT Air Asia did not go bankrupt during the period, PT Indonesia Transport & Infrastructure went bankrupt during the period, while the other two companies had experienced bankruptcy between the 3 periods. Based on the Springate method, the companies PT Garuda Indonesia and PT Indonesia Transport & Infrastructure experienced bankruptcy during the period, PT Air Asia Tbk did not go bankrupt during the period, while PT Jaya Trishindo experienced bankruptcy in the 2020 and 2021 periods. The method is used as a bankruptcy estimate, it cannot be said to be 100% accurate. Bankruptcy can occur with various factors that are not suspected by the company. further researchers can develop research by adding bankruptcy prediction methods and can add observation periods.

Perbandingan Prediksi Kebangkrutan Menggunakan Metode Grover, Metode Altman Z-Score dan Metode Springate

This study aims to analyze the bankruptcy rate of companies using the Grover model, Altman Z-score model, and Springate model. This type of quantitative research with the population uses companies that conducted IPOs in 2021. The sampling technique used is purposive sampling and the number of samples obtained based on predetermined criteria is 54 company samples. The results of this study showed that sample companies that predicted bankruptcy using the Grover method had an accuracy rate of 83%, and an error rate of 17%. The Altman Z-score method has an accuracy rate of 87%, an error rate of 9%, and a gray area rate of 4%. The Springate method has the highest accuracy rate of 94%, and an error rate of 6%. This bankruptcy prediction analysis can help company owners to immediately anticipate the factors that cause bankruptcy and for investors can be used as information in making decisions to invest in shares in IPO companies in 2021.

Bankruptcy prediction using machine learning models with the text-based communicative value of annual reports

We investigate whether including the text-based communicative value of annual report increases the predictive power of four machine learning models (Logistic regression, Random Forest, XGBoost, and Support Vector Machine) for corporate bankruptcy prediction using U.S. firm observations from 1994 to 2018. We find that the overall prediction effectiveness of these four models (e.g. accuracy, F1-score, AUCs) significantly improves, especially true in the performance of XGBoost and Random Forest models. In addition, we find that annual report text-based communicative value variables significantly reduce models’ Type II error and keep the Type I error at a relatively small level, especially for the short-term bankruptcy forecast. The results reveal that annual report text-based communicative value effectively mitigates the model misidentification of a non-bankrupt firm as a bankrupt firm. Our results also suggest that annual report text-based communicative value is helpful for bank’s corporate loan underwriting decisions. Finally, our findings still hold when considering different testing periods and random state settings, replacing by another publicly available bankruptcy dataset, and introducing neural network models.

Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm.

The prediction of imminent bankruptcy for a company is important to banks, government agencies, business owners, and different business stakeholders. Bankruptcy is influenced by many global and local aspects, so it can hardly be anticipated without deeper analysis and economic modeling knowledge. To make this problem even more challenging, the available bankruptcy datasets are usually imbalanced since even in times of financial crisis, bankrupt companies constitute only a fraction of all operating businesses. In this article, we propose a novel bankruptcy prediction approach based on a shallow autoencoder ensemble that is optimized by a genetic algorithm. The goal of the autoencoders is to learn the distribution of the majority class: going concern businesses. Then, the bankrupt companies are represented by higher autoencoder reconstruction errors. The choice of the optimal threshold value for the reconstruction error, which is used to differentiate between bankrupt and nonbankrupt companies, is crucial and determines the final classification decision. In our approach, the threshold for each autoencoder is determined by a genetic algorithm. We evaluate the proposed method on four different datasets containing small and medium-sized enterprises. The results show that the autoencoder ensemble is able to identify bankrupt companies with geometric mean scores ranging from 71% to 93.7%, (depending on the industry and evaluation year).

Predicting Corporate Bankruptcy in JSE-AltX Listed Firms

ABSTRACT: The objective of the study is to predict corporate bankruptcy on the JSE-AltX (Alternative Stock Exchange) listed firms by employing Discriminant Analysis (MDA). Since its inception, the JSE-AltX has listed more than 100 firms and majority of them have migrated to the JSE main board. Motivated by the increasing number of delisted firms both pre and post their migration from JSE-AltX to the JSE main board, this study seeks to investigate the financial distress in the JSE-AltX listed firms. This study use a quantitative method to predict bankruptcy for a sample of 20 JSE-AltX listed firms that belongs to a wide range of industries, over the period from 1 January 2004 until 31 December 2015. Based on the previous literature it is evident that financial ratios play a significant role in the prediction of financial distress. Employing the same set of financial ratios (extracted from annual reports mainly the audited consolidated balance sheets and income statements) that are used in bankruptcy prediction as independent variables, the empirical results show that MDA has a statistically significant power in predicting default risk on the JSE-AltX listed firms. The findings show that the discrimination function is significant at the 5% level of significance. The MDA results reveal that, 7 out of the 20 sample firms are prone to bankruptcy, while the rest are not. Furthermore, the model classifies net profit margin (short-term profitability), current ratio (liquidity) and return on capital invested as the most important financial ratios in distinguishing the successful firms from unsuccessful firms post migration from the JSE-AltX to the JSE main board. Generally, this study results have policy implications which regulatory authorities, investors, employees and lenders will find interesting. Firstly, regulatory authorities can find this research useful as it provides effective review of the firm's financial distress conditions and subsequently signals default risk to various stakeholders. Secondly, this research will not only assist in identify potential default risks, but it will also enable different stakeholders to formulate mechanisms or relevant policies and procedures that will allow them to detect and prevent financial distress.

Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia

Abstract Research purpose. Financial distress being a global phenomenon makes it impact firms in all sectors of the economy and predicting corporate bankruptcy has become a crucial issue in economics. At the beginning of the last century, the first studies aimed to predict corporate bankruptcy were published. In Slovakia, however, several prediction models were developed with a significant delay. The main aim of this paper is to develop a model for predicting bankruptcy based on the financial information of 3,783 Slovak enterprises operating in the manufacturing and construction sectors in 2020 and 2021. Design / Methodology / Approach. A prediction model that uses the appropriate financial indicators as predictors may be developed using multiple discriminant analysis. Multiple discriminant analysis is currently used in prediction model development. In this case, financial health is assessed using several variables that are weighted in order to maximise the difference between the average value calculated in the group of prosperous and non-prosperous firms. When developing a bankruptcy prediction model based on multiple discriminant analysis, it is crucial to determine the independent variables used as primary financial health predictors. Findings. Due to the discriminant analysis results, the corporate debt level of the monitored firms may be regarded as appropriate. Despite the fact that the model identified 215 firms in financial distress due to an insufficient debt level, 3,568 out of 3,783 Slovak enterprises operating in the manufacturing and construction sectors did not have any problems with financing their debts. The self-financing ratio was identified in the developed model as the variable with the highest accuracy. Based on the results, the developed model has an overall discriminant ability of 93% since bankruptcy prediction models require strong discriminating abilities to be used in practice. Originality / Value / Practical implications. The principal contribution of the paper is its application of the latest available data, which could help in more accurate financial stability predictions for firms during the current difficult period. Additionally, this is a ground-breaking research study in Slovakia that models the financial health of enterprises in the post-pandemic period.

CORPORATE BANKRUPTCY PREDICTION: A SYSTEMATIC LITERATURE REVIEW AND COMPREHENSIVE BIBLIOMETRIC ANALYSIS

Researchers from all over the world have become more interested in bankruptcy prediction during the past 50 years. The prediction of corporate financial difficulties has been the subject of numerous studies ever since Altman revealed the breakthrough bankruptcy prediction model in 1968. The main aim of this research paper is to describe the fundamental concepts associated with the subject of corporate bankruptcy prediction. By identifying the most relevant research papers, nations, and authors in the Web of Science database, an in-depth review of the publications was performed before the analysis. The bibliometric map was created in the VOS Viewer program using the final search result with all available information. The results of the bibliometric analysis reveal that the keywords bankruptcy prediction and classification are the most closely related keywords using the analysis of citations that frequently occur, and that the USA and China developed the most significant international co-author relationships.

Determining the Bancrupt Contingency as the Level Estimation Method of Western Ukraine Gas Distribution Enterprises’ Competence Capacity

The purpose of this work is to deepen the methodological provisions and develop practical recommendations with the purpose of increasing the competitiveness level of gas distribution network operators. To achieve this goal by systematizing and unifying different approaches, we developed our own approach to determining the competitiveness level of gas distribution network operators. Nowadays, most approaches and methods are formed with the obligatory use of expert assessment methods, which, in its turn, predetermines relatively subjective judgments and results. The results of our research have proven the significantly increased exigency for measures to prevent possible bankruptcy of gas distribution network operators functioning in the western region of Ukraine with the practical application of both foreign and domestic approaches. In the process of conducting a comprehensive analysis of financial and economic indicators and those reflecting the results of economic activity of gas distribution network operators functioning in the western region of Ukraine, the following approaches have been used in our study with the involvement of: 1) E. Altman’s Two-Factor Model; 2) E. Altman’s Five-Factor Model (Altman, 1977; 2020); 3) R. Lis’s bankruptcy prediction model; 4) Richard Taffler’s model; 5) W. Beaver’s coefficient; 6) O. Tereshchenko’s model; 7) Matviychuk’s model. Ultimately based on the study above, the following conclusions can be drawn as a method of assessing the of gas distribution network operators’ competitiveness: the existing models for diagnosing bankruptcy of enterprises are characterized by ambiguity; as for example, if R. Lis’s model indicates a low bankruptcy level, then other models prove the opposite situation; domestic diagnostic models need to be improved, as they were developed in the early 2000s and disregard current trends in functioning of enterprises. The article examines the methodological approaches to the level of competitiveness assessment of gas distribution network operators functioning in Ukraine. In our opinion, this fully corresponds to the thematic focus of the journal.

Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk.

Analisis Prediksi Kebangkrutan Dengan Menggunakan Model Altman Z-Score Pada Pt Tirta Mahakam Resources Tbk Tahun 2017-2021

The purpose of this study is to determine and analyze bankruptcy prediction at PT. Tirta MahakamResources Tbk for the 2017-2021 period. This type of research is included in the category of quantitativedescriptive research. The research data source is the company's annual financial report data for the period2017 - 2021 which has been audited and can be accessed via the website www.idx.co.id. The samplingtechnique used is porposive sampling technique. The analysis technique used in this study is the Altman ZScore model using ratios (Net Working Capital to Total Assets, Earnings Before Tax to Total Assets,Retained Earnings to Total Assets, Sales to Total Assets, and Total Equity to Total Assets). The results ofthe analysis show that the model can adapt to the environment and economic conditions in Indonesia as anearly warning system to mitigate future bankruptcy risks, so that management can immediately takepreventive action

Bankruptcy Prediction Using the Altman Z-Score Method for Companies Listed on the Indonesian Stock Exchange

Bankruptcy Prediction Using the Altman Z-Score Method for Companies Listed on the Indonesian Stock Exchange