Bankruptcy Prediction Research Articles

FAKTOR-FAKTOR YANG MEMPENGARUHI PREDIKSI KEBANGKRUTAN PERUSAHAAN PADA PERUSAHAAN MANUFAKTUR

This research aims to examine the effect of cash flow, profitability, and leverage on the predicition of corporate bankruptcy in manufacturing companies listed on the Indonesia Stock Exchange for the period 2018-2020. This study used a purposive samnpling method and obtained 19 companies and total of samples in this study were 57 samples. The method in this study used logistic regression analytics with Eviews12 and Microsoft Excel. The results of this study shows that cash flow, profitability, and leverage has an insignificant effect on prediction of corporate bankruptcy. Then, the cash flow variables. profitability, and leverage simultaneously has an effect on prediction of corporate bankruptcy.

Efficiency of Working Capital & Assets Management in the Function of SMEs Bankruptcy Prediction

Research Question: This research has been conducted with the aim of determining whether it is possible to generate a model that can reliably predict bankruptcy of Serbian small and medium-sized enterprises (SMEs) using Working Capital Management (WCM) and Asset Management (AM) efficiency ratios. Motivation: Motive for this research is the fact there are not many business failure prediction models related to SMEs. In addition, existing models are not focused on efficiency of the two above mentioned categories. Also, previously developed models, especially traditional and ground-breaking ones, are not necessarily aligned with accounting procedures and economic environment of all countries, which indicates the need to develop a model that is adapted for Serbian territory. Idea: The idea is to develop a model that has the ability to predict whether an entity has a tendency to initiate bankruptcy proceedings in the next year. This is useful both for external stakeholders and for SMEs’ owners themselves, as it allows them to better manage resources. Data: The research was conducted on a sample of 100 Serbian SMEs. Data for the calculation of ratio indicators is available on the Business Registers Agency webpage. Tools: The research was conducted as a combination of financial and statistical analysis instruments. Ratio indicators were used for financial analysis part, while statistical analysis was conducted in SPSS program v.26 and includes logistic (binary) regression. Findings: Results of the research indicate that AM efficiency indicators are suitable for SMEs bankruptcy prediction modelling, but also indicate that WCM ratios don’t have great contribution in bankruptcy prediction for Serbian SMEs. A model that has a classification accuracy of 79% has been developed. Contribution: This research empirically tests how selected ratio indicators support SMEs bankruptcy prediction, and therefore can be beneficial for all SMEs stakeholders, but also other researches since the research methodology is explained in details.

Predicting Business Bankruptcy in Colombian SMEs: A Machine Learning Approach

In this research paper, we address the challenge of predicting business bankruptcy in small and medium-sized enterprises (SMEs) in Colombia. We analyze various financial and non-financial factors that influence the likelihood of bankruptcy and employ machine learning techniques to improve prediction accuracy. We construct a database of 62,500 SMEs for the period 2017–2021 and compare two estimation methods: logistic regression and the eXtreme Gradient Boosting (XGBoost) algorithm. The findings demonstrate that the XGBoost algorithm outperforms in bankruptcy prediction. Key financial variables such as profitability and access to working capital, as well as non-financial variables such as geographic location, are identified as influencing bankruptcy risk. These findings provide valuable insights for stakeholders such as managers, financial intermediaries, and governmental decision-makers in their efforts to support and finance SMEs in Colombia, aiming to reduce bankruptcy rates and promote their economic success.

The evaluation of bankruptcy prediction models based on socio-economic costs

The evaluation of bankruptcy prediction models based on socio-economic costs

BANK NTB SYARIAH'S SOUNDNESS: A MULTI-METHOD ASSESSMENT USING CAMELS, RGEC, AND ALTMAN Z-SCORE

This paper endeavors to appraise the level of stability of Bank NTB Syariah during the time span of 2019 to 2021, by employing the CAMELS technique (which scrutinizes the bank's Capital, Assets, Management, Earnings, Liquidity, and Market Sensitivity), RGEC (which assesses the Risk Profile, Good Corporate Governance (GCG), Earnings, and Capital), as well as the Altman Z Score method (which analyzes the potential for bankruptcy). The data utilized for this research were procured through the application of documentation techniques and sourced from the financial statements and annual reports of Bank NTB Syariah, from 2019 to 2021. The data analysis technique utilized involved an evaluation of the bank's level of stability, utilizing a risk-based approach, encompassing the CAMELS, RGEC, and Altman Z Score factors. Based on the appraisal of the level of stability of PT. Bank NTB Syariah from 2019 to 2021, as evaluated by the comprehensive RGEC and CAMELS approach, the bank is deemed to be in a sound financial condition. However, the results of the bankruptcy prediction analysis, utilizing the Altman Z Score model, indicate that the financial state of Bank NTB Syariah in 2021 is situated in a nebulous area, albeit superior to its condition in 2020 and 2019, which indicated an indication of financial distress and potential bankruptcy.

Machine Learning Model Using Extreme Gradient Boosting (XGBoost) Feature Importance and Light Gradient Boosting Machine (LightGBM) to Improve Accurate Prediction of Bankruptcy

Abstract. Humans have limitations in processing and analyzing large amounts of data in a short time, including in terms of analyzing bankruptcy data. Bankruptcy data is one of the data that has complex information, so it requires technology that can assist in the process of analyzing and processing data more quickly and efficiently. Data science technology enables data processing and analysis on a large scale, using parallel processing techniques. Parallel processing can be implemented in machine learning models.
 Purpose: Using parallel processing techniques, data science technologies enable data processing and analysis at scale. Parallel processing can be implemented in machine learning models. Therefore, this study aims to implement a machine learning model using the Light Gradient Boosting Machine (LightGBM) classification algorithm which is optimized using Extreme Gradient Boosting (XGBoost) Feature Importance to increase the accuracy of bankruptcy prediction.
 Methods/Study design/approach: Bankruptcy prediction is carried out by applying LightGBM as a classification model and optimized using the XGBoost algorithm as a Feature Importance technique to improve model accuracy. the dataset used is the Taiwanese Bankruptcy dataset collected from the Taiwan Economic Journal for 1999 to 2009 and has 6,819 data. Taiwanese Bankruptcy is unbalanced data, so this study applies random oversampling.
 Result/Findings: The results obtained after going through the model testing process using the confusion matrix obtained an accuracy of the performance of LightGBM+XGBoost Feature Importance of 99.227%.
 Novelty/Originality/Value: So it can be concluded that the implementation of XGBoost Feature Importance can be used to improve LightGBM's performance in bankruptcy prediction.

Multiperiod Bankruptcy Prediction Models with Interpretable Single Models

This study considers multiperiod bankruptcy prediction models, an aspect scarcely considered in research despite its importance, since creditors must assess the risk of loans over the entire life of the debt and not at a specific point in the future. Two possibilities for the implementation of multiperiod prediction models are considered: Multi-Model multiperiod Bankruptcy Prediction Models (MMBPM) and Single-Model multiperiod Bankruptcy Prediction Models (SMBPM). The former considers the conditional probabilities obtained by individual models predicting bankruptcy at specific times in the future, while the latter is a single model predicting bankruptcy at a specific time interval in the future. The results show that there are no significant differences between the two approaches when compared using data after the learning period. However, SMBPMs have the important advantage of interpretability for decision-making, which is discussed with examples. Moreover, a comparison of SMBPM performance with external references is performed.

Handling outliers in bankruptcy prediction models based on logistic regression

The primary tool for managing bank default risk is the credit rating of potential customers. The focus of the present study is on the logistic regression method used to construct 95% of the lender scorecards. The aim of the research is to determine how much the treatment of outliers improves the classification accuracy of the models when using a method that is highly sensitive to outliers, and which method of treating outliers results in the highest classification accuracy. Furthermore, what criteria should be used to determine the cut-off value of the models for a sample that does not contain solvent and insolvent businesses in equal proportions. The analysis was carried out on a sample of 1677 construction companies. The results show that the treatment of outliers significantly improves the predictive ability of the models, while the replacement of outliers with the closest non-outlier proved to be the most effective for treating outliers. When determining the cut-off, it is inappropriate to use the value that results in the highest classification accuracy, as this may lead to an increase in the proportion of first-order errors. The optimisation of this value may depend on the degree of credit risk taken by a given financial institution in its portfolio of loans.

THE IMPACT OF THE ALTMAN SCORE ON THE ENERGY SECTOR COMPANIES

The financial literature regarding bankruptcy prediction models includes the application of several statistical tools which have progressively become accessible. This paper explores the impact of the Altman score on the stock prices of the Romanian companies listed on the Bucharest Stock Exchange during the period 2014-2022. The Altman score is calculated based on the annual reports of the analyzed companies collected from the Bloomberg database, while the stock exchange prices are collected from the Euromonitor database. The methodology used in the analysis is panel regression realized with the Generalized Method of Movements technique. The data is processed using the EViews software. This research study shows that there is a negative correlation between the Altman Z score and the companies’ share price. In terms of theoretical contribution, this study investigates one of the bankruptcy prediction models that is used more often than other models, namely the Altman Z-Score.

Business bankruptcy prediction: the multisectorial models’ efficiency in the Old Blue Economy Iberian companies’

In the risk evaluation by the stakeholders it’s crucial to understand the causes of bankruptcy or breach in corporate sustainability, as well as more efficient ways of predicting it. These techniques, mainly supported by financial analysis, over time, have given rise to a diversity of approaches in particular to the Multivariate Discriminant Analysis. We selected, 54 iberian companies from that sector, divided them in two samples, one of the bankrupt companies and another of the healthy ones, matched by size and business' volume. Were applied 21 multisectoral models from a diversity of countries abd time horizons, with greater presence in the literature. As conclusions, continuing Peres and Antão (2019) the work, were identified the best bankruptcy or sustainability predictors, to the Old Blue Economy Iberian industries’, up to 5 years in advance.

Company Bankruptcy Prediction -Focus on Liability and Working Capital Analysis

Company bankruptcy is a way of corporate restructuring, and bankruptcy prediction on various data of the enterprise in advance can avoid greater losses. The topic of this article also revolves around corporate bankruptcy predictions. This paper focuses on the screening of financial indicators related to corporate bankruptcy predictions, with a focus on the analysis of highly correlated data. In this work, SPSS is first used to screen the company's financial operation index data, and then focus on the data related to liabilities and working capital to carry out the company's bankruptcy prediction analysis, and it is concluded that these two indicators have a high impact on the company's operation and bankruptcy prediction. Its significance is that it has certain reference value for the subsequent decision-making of enterprises.

Company Bankruptcy Prediction

Following the founding and operation of a corporation, the biggest challenge it faces is the risk of bankruptcy, so how to prevent, controlling, and analyze the causes of bankruptcy is one of the most critical tasks for every company. As we all know, many factors affect corporate bankruptcy, and most studies are based on a unilateral or small range of factors set to study its impact on bankruptcy. Based on the data provided by Taiwan Economic Journal from 1999 to 2009, this paper adopts univariate and multivariate analysis models to analyze the data. A 3Comparative analysis, Normality test, T-test, non-parametric test, ANOVA test, Factor analysis and logistics were carried out using SPSS auxiliary tools Regression (Regression) controls relatively comprehensive factors that affect a company's bankruptcy from an overall perspective. It aims to demonstrate the selection of bankruptcy factors and analysis ideas. After analysis, we conclude that through the research and analysis, we have a new understanding of the causes of corporate bankruptcy. We find the four most important factors affecting corporate bankruptcy. Namely, ROAC before interest and depreciation before interest, Liability to Equity, Total assets to GNP price, and No credit interval, In addition, many single factors may have no impact on a company's bankruptcy. However, when they coexist with other factors, they will have a chain effect, resulting in even multiple amplification of the risk of bankruptcy. Therefore, different from the specific analysis of the impact of single factors on the bankruptcy of a company, this paper extracts the primary factors and takes the analysis of the relationship between individual factors as a specific example by showing the research perspective of innovation to propose the actual operation of a company from the macro and micro aspects, that is, to clarify which departments and decisions to pay special attention to in daily operation.

Prediction model of financial distress reporting of multifinance companies in 2017-2021 in Indonesia

The urgency of this study is because the phenomenon of the Covid-19 pandemic is the cause of many multifinance companies to have an impact on decreasing the quality of financial performance so that it has the potential for financial distress, while the purpose of this study is to determine and analyze the effect of liquidity, solvency and profitability on one of the 7 most accurate bankruptcy prediction models. The sample method used is a purposive sampling technique with criteria, namely (1) companies engaged in financing (multi finance), (2) Meeting data needs, from 100 samples get 34 companies, analyzing data calculations by calculating financial performance and 7 bankruptcy prediction models, 1 bankruptcy prediction model is chosen that is the most accurate with the lowest number of companies predicting risk to see the effect using data regression panel because the financial statements are reviewed in a time series of 5 years period, the research method used with the number of research samples is 34 companies from 100 populations, with a comparative analysis method of 7 bankruptcy models, influence tests with classical assumption tests and regression panel data because the financial statements are reviewed in a 5-year time series period, the results obtained are that the analysis of financial performance is based on predetermined ratios Describes an increase every year in several companies, and indicates that 34 companies have very good financial positions. bankruptcy analysis from 7 methods, there are 3 companies 3 times in a row to get distress conclusions, namely Bosowa Multifinance, Anugerah Buana Central Multi Finance and Batavia Prosperindo Finance Tbk. In addition, the relevant bankruptcy prediction in this study uses a Z Score where the results are assessed because a positive Z Score value indicates a lower level of bankruptcy risk, The results of the influence test result that the variables of liquidity, solvency and profitability affect the Bankruptcy Prediction, the managerial implications obtained even though the company is considered good but still must be controlled and evaluated financial management every period.

Bankruptcy prediction with low-quality financial information

The corporate bankruptcy prediction literature has traditionally relied on data from public, audited companies. However, the vast majority of firms worldwide are privately-held and lack the same level of scrutiny over their financial statements. As a result, these businesses usually produce less accurate and transparent accounting reports. Our research problem is to address this gap: how stakeholders deal with these less reliable information? Using a novel dataset of 503 private firms that filed for reorganization in Brazil between 2007 and 2020, we found that financial ratios had a significantly lesser effect on explaining default and bankruptcy than what previous research suggested, due in part to the lower information content in the accounting statements within our database. Instead, lenders seem to focus on harder-to-conceal variables, such as collateralizable assets, as well as on institutional factors, like proxies of financial statement quality. There is also concerning evidence that specialized attorneys can ”work the system” in favor of distressed companies regardless of their financial fundamentals. Additionally, we found that machine learning models outperformed traditional statistical ones in different sorts of metrics, corroborating the literature on the superior performance of non-linear approaches on datasets having synergistic causality among its features.

Bankruptcy prediction using machine learning and Shapley additive explanations

Bankruptcy prediction using machine learning and Shapley additive explanations

The Role Of Free Cash Flow, Bankruptcy Prediction and The Audit Committee In Earnings Management

The Role Of Free Cash Flow, Bankruptcy Prediction and The Audit Committee In Earnings Management

Tax Variances and Financial Performance of Deposit Money Banks Multinational Companies in Nigeria

The study examined the effect of tax variance and financial performance of deposit money banks multinational companies in Nigeria. It specifically examined the effect of book tax difference on return of asset of deposit money banks, it also examined the effect of effective tax rate on return of asset of deposit money banks in Nigeria .Ex-post facto research design was employed through secondary data to establish the relationship between dependent variable (Return on Asset) and independent variables (Book tax differences and effective tax rate) The population of this study covers all the 14 deposit money banks listed on the Nigeria Exchange Group as at 31 st December, 2020. The study used census sampling to cover all the 14 deposit money banks. Data on all the explained and explanatory variables were extracted from the published financial statements of the 14 deposit money banks for the period of 2006-2020 with 90 observations. Data were analyzed using Panel data which consist on correlation, multiple regression were used to analyze the data. The study found that book tax difference has positive and significant effect on return on asset of deposit money banks while effective tax rate was negative and insignificant on return of asset of listed deposit money banks. Consequent on the findings, the study concluded that tax variance has significant effect on financial performance of listed deposit money banks in Nigeria. The study recommends amongst others that policy makers, accounting standards developers and industry regulators can utilize the study findings to develop an insight on industry effect of book tax difference for ease of bankruptcy prediction from financing cash flow deficiency.

How do bankruptcy risk estimations change in time? Empirical evidence from listed US companies

The study analyzes how bankruptcy risk estimations of listed US companies changed in the period January 2006 to May 2023. Based on multi-year data series of bankruptcy risk estimations, the study shows the dynamics of bankruptcy risk estimations and, therefore, helps interpret the outcome of bankruptcy prediction models and evaluate the recent financial distress that a notable number of listed US companies exhibits. Furthermore, the study analyzes the pre-bankruptcy patterns of listed US companies that filed for bankruptcy in 2023.

PREDIKSI KEBANGKRUTAN DAN PENGARUHNYA TERHADAP NILAI PERUSAHAAN PT. UNILEVER INDONESIA TAHUN 2022

This study aims to analyze and determine the company's financial performance, potential bankruptcy with the Altman Z-Score Modification approach and analyze the effect of bankruptcy predictions on the value of firm in PT. Unilever Indonesia Tbk in 2022. The method used in the study with the Altman Z-Score Modified method. This Altman model is very useful for companies to find out the condition of the company during the current period as well as simple linear regression with IBM SPSS Statistic 26 software. The data in this study was obtained by processing data on the quarterly financial statements of PT. Unilever Indonesia Tbk for the period of Q1 2015 to Q2 2022. The results showed in regression research, altman Z-Score's potential bankruptcy had a significant negative effect on the value of the firm.

Prediction of Bankruptcy and Impact of Credit Risk Management on Profitability of Commercial Banks in India: A Study

Banking system of a country plays a leading role in its economic development. India is also not isolate from such fact. Indian banking system plays a crucial and impactful role in both Individual and economy progress. Since the huge major flow of money into economy is controlled by banking system in India, the system policies, operational efficiencies are having linear impact on the economic development of the country. The efficiency of the banking industry of the country is the sign of economic development and public commercial banks will have to face the competition of private commercial banks as well. Hence, the present study objective is to predict the solvency condition of Indian public commercial banks and the impact of credit risk management on the profitability of commercial banks in India. To fulfil the objectives of the study, the study has been used the secondary data extracted from annual reports of the top three public commercial banks for the period from 2012 to 2022. The data were analysed using a descriptive statistics and panel data regression model and the bankruptcy is predicted using Altman’s Z Score model. the results found that, public sector Indian commercial banks under the pressure to experience the bankruptcy and there is no significant impact of credit risk management on profitability of commercial banks in India.