Bankruptcy Analysis Research Articles

ANALISIS PREDIKSI FINANCIAL DISTRESS DENGAN MODEL ALTMAN Z - SCORE PADA PT. SMARTFREN TELECOM TBK PERIODE 2017-2023

Smartfren Telecom Tbk. is one of the telecommunications companies in Indonesia. The company has experienced losses in the last seven periods, from 2017 to 2023. It is feared that this condition will result in a high risk of a company experiencing financial distress or even bankruptcy. This research aims to find out, describe and explain the results of applying the analysis of the financial distress prediction model, namely the Altman Z”-Score model which is used to assess and predict potential bankruptcy with research objects at PT. Smartfren Telecom Tbk for the 2017-2023 period. The method used in this research is a descriptive method using a qualitative approach, and the operational variables used are independent variables, namely a bankruptcy prediction model with the dependent variable being financial ratios. The data used is secondary data in the form of PT's annual financial report. Smartfren Telecom Tbk for the 2017-2023 period. Results of financial distress analysis using the Altman Z”-Score model at PT. Smartfren Telecom Tbk for the 2017-2023 period, shows that the company is in a state of distress because the average Z"-Score value is -2.9 or Z < 1.1. This research shows that analysis of bankruptcy or financial distress using the Altman Z"-Score model at PT. Smartfren Telecom Tbk for the 2017-2023 period concluded that the company was in a state of distress.

Bankruptcy Analysis Using the Altman Z-Score Model (Study on Retail Companies Listed on the Indonesia Stock Exchange)

Bankruptcy is a legal or economic event that often results in the complete cessation of all business activities and operations. This bankruptcy usually starts with liquidity problems, but is closely linked to situations where a company's debt exceeds its total assets. The objective of this study is to evaluate the effectiveness of the Altman Z-score model as a tool to predict bankruptcy. The main focus of this study is retail companies listed on the Indonesia Stock Exchange in 2021-2023. 31 companies participated in the study, supported by 93 financial statements. The researchers used purposive sampling method for the sampling process. The study uses the modified Altman Z-score method (1995) to analyze the obtained data. The analysis revealed that 17 companies (54.8%) could be classified as healthy. 3 companies (9.7%) were classified as weak, and 11 companies (36.7%) were expected to go bankrupt. These results provide insight into the financial health of the retail companies surveyed and highlight the importance of using predictive models to determine bankruptcy risk.

Analisis Financial Distress dengan Model Altman Z Score pada Sektor Perkebunan di Bursa Efek Indonesia Periode 2014-2022

This research aims to analyze the comparative analysis of potential bankruptcy (financial distress) models using the first, revised and modified Altman model for plantation sector companies on the Indonesia Stock Exchange. Financial distress can be used to determine and monitor a company's financial condition, so that it is hoped that they will be able to make better decisions to maintain the stability of company performance, including in public, private, manufacturing or service companies of various sizes. This research uses data and facts related to 11 companies from 21 listed companies in the plantation sector on the Indonesia Stock Exchange. The results of the research show that the issuer shares that have the best financial performance based on the financial distress analysis respectively and those that have not experienced bankruptcy are the LSIP and SMAR issuers, while the ones that have experienced the least pressure are the AALI issuers

Bankruptcy Analysis of Polish Waste Management Companies

The waste management system should be constantly monitored to ensure rational waste management, limit the possibility of contamination of surface and groundwater, reduce the risk of waste fires and illegal storage. Unfortunately, in many cases this system remains without constant supervision, and subsequent waste management enterprises remain bankrupt, are closed or are subject to bankruptcy proceedings. Main aim of this paper is the construction of the author's model of financial bankruptcy prediction dedicated to the specificity of waste management companies activity. That discriminatory model will allow entities dealing with waste management to be given early warning of the risk of bankruptcy and for them to take corrective actions that will allow them to continue their very important activities aimed at protecting the natural environment. Based on the backward stepwise regression procedure on a group of 136 bankrupt and 136 still operating enterprises from the analyzed sector of economy, a discriminatory model was proposed. It was able to accurately predict the bankruptcy/continuation of business activity of over 76% of the analyzed waste management enterprises. The effectiveness of the predictions of the original model is much higher than the predictions calculated using other, popular discriminatory models. Obtained research results indicate that the proposed models should be constantly developed for waste management companies, not only for economic reasons, but above all for environmental reasons.

Development of fuzzified neural network for enterprise bankruptcy risk estimation

The object of this study is the assessment of the level of enterprise bankruptcy risk. It is a critical component in assessing the financial condition of an enterprise, and also serves as an indicator that allows the management team to reduce potential risks and develop their own strategies to strengthen the financial condition of the enterprise. One of the most challenging aspects of bankruptcy forecasting is the complex financial situations of bankrupt companies. By accurately predicting the risk of bankruptcy, businesses can take preventive measures to mitigate financial difficulties and ensure long-term sustainability. Previous methods, such as Altman's Z-score, are not accurate enough, as presented in the study. The paper investigates a modern approach to bankruptcy prediction based on a neural network with complex neural elements, namely neural arithmetic logic units (NALUs) and a custom phasing layer. This layer can process complex raw numerical values, such as financial indicators relevant to the analysis of a company's bankruptcy. Compared to Altman's Z-score, the developed method demonstrates a better F1 score in bankruptcy classification (48 %). On the raw data, the neural network demonstrates an improvement in the F1 score by about 40 % compared to the classical multilayer perceptron (MLP) with linear layers and nonlinear activation functions. A modern replacement for ReLU called Mish was used, which achieves better generalization. It was also assumed that the addition of new neural elements, which provide the neural network with arithmetic capabilities, contributes to the performance of processing non-normalized input data. This work highlights the importance of using advanced neural network architectures to improve the accuracy and reliability of forecasting in financial risk assessment. Using the parameters presented in the study, managers of enterprises will be able to more accurately assess the risk of bankruptcy.

Examining the financial landscape of cancer: An analysis of debt and bankruptcy among patients in Massachusetts (2010-2019).

11032 Background: Financial toxicity is commonly reported by patients following a cancer diagnosis. Prior patient surveys indicate that up to one-third of cancer patients incur medical debt, of which 55% carry balances exceeding $10,000. In addition to debt and bankruptcy, credit scores are an important indicator of socioeconomic well-being following cancer diagnosis, because they affect housing stability, transportation access, and may even be predictive of health. Limited studies quantify incurred debt, bankruptcy rates, or credit scores following a cancer diagnosis compared with patients without a cancer diagnosis. Methods: Individuals with a cancer diagnosis in Massachusetts from 2010 to 2019 were identified through the Massachusetts Cancer Registry, and patient-level records on debt, bankruptcy, and other measures of financial well-being were linked using data from a major United States credit bureau. Changes in medical debt, credit score and bankruptcy filings were evaluated over time and compared with a random sampling of individuals residing in Massachusetts over the same time period that did not have a cancer diagnosis and were matched based on demographic factors. Results: 99,175 individuals with cancer and 188,875 individuals without cancer were identified, and average annual total debt balances were $758 and $669, respectively, throughout the study period. The highest total debt balances were observed among individuals with thyroid cancer ($913). Average annual medical collection balances were $45 for individuals without cancer and $52 among those with cancer with the highest balances among those with liver cancer ($66). Across the study period, the average annual bankruptcy incidence rate per 100 persons was 7.15 for those without cancer and 6.22 among those with cancer, with the highest incidence rates among those with thyroid cancer (7.34). Although there was a gradual increase in credit score in the control population, survivors of bladder, liver, ovarian, colorectal and lung cancers experienced a decline in credit scores. Conclusions: High rates of financial toxicity among individuals with cancer suggest that widespread healthcare and consumer finance reforms have not yet fully alleviated medical debt and bankruptcy in Massachusetts. Further research is needed to understand drivers of financial burden over time among those with cancer.

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale.

Prediction and Comparison of Bankruptcy Models in Banking Sector Companies in Indonesia

The Covid-19 pandemic, coupled with the economic recession from 2019 to 2022, has adversely impacted various industries, leading to bankruptcies. Among the affected sectors, the banking industry faced significant challenges, experiencing disruptions in credit processes and fund distribution due to diminished purchasing power. This situation poses a severe threat to the banking sector, necessitating the monitoring of financial conditions through bankruptcy analysis as an early warning system for company performance. This study investigates potential differences in the results of financial distress prediction models—Altman, Springate, Zmijewski, Grover, and Ohlson—during the abnormal conditions of the Covid-19 pandemic. Using purposive sampling of banking companies listed on the IDX from 2019 to 2022, the study divided the samples into financial distress and non-financial distress categories. Analysis involved tests for multicollinearity assumptions, logistic regression, and accuracy/error rate calculations. The findings reveal variations in the predictive abilities of the Altman, Springate, Zmijewski, Grover, and Ohlson models. The Grover model emerged as the most accurate, with a 60% accuracy rate in predicting bankruptcy, while Altman, Springate, and Zmijewski models demonstrated low predictive values (0%, 0%, and 15% accuracy, respectively). The simplicity of the Grover model's measurement indicators, incorporating capital adequacy, EBIT, and ROA, offers a comprehensive view of bankruptcy prediction ratios. Moreover, stringent internal risk analysis and external factors, such as regulatory interventions, contribute to keeping banks resilient amid global economic crises. The research suggests that banking companies can benefit from employing the Grover method for bankruptcy analysis as part of their future anticipation strategy.

Case Analysis of Bankruptcy of Baoshang Bank

Case Analysis of Bankruptcy of Baoshang Bank

What are the characteristics of a survivor Evidence from bankruptcy analysis of newly formed Japanese SMEs

What are the characteristics of a survivor Evidence from bankruptcy analysis of newly formed Japanese SMEs

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ПРОВЕДЕНИЯ АНАЛИЗА ДАННЫХ В РАМКАХ ДЕЛ О БАНКРОТСТВЕ

The article discusses the use of artificial intelligence in the field of data analysis in bankruptcy cases, provides several examples and possible tables demonstrating the use of artificial intelligence in this area. The effectiveness of using this tool as a means of automating monitoring of the financial condition of companies was assessed. The paper highlighted the key benefits of using artificial intelligence in the context of bankruptcy, including process automation, increased security and data analysis to improve decision making. The article highlighted potential problems and limitations associated with the use of artificial intelligence, such as the need to constantly update algorithms and data to keep analysis current and accurate. In addition, the work puts forward assumptions about potential directions for the development of artificial intelligence and its gradual introduction into the bankruptcy analysis process.

What are the characteristics of a survivor Evidence from bankruptcy analysis of newly formed Japanese SMEs

What are the characteristics of a survivor Evidence from bankruptcy analysis of newly formed Japanese SMEs

Analysis Financial Distress Potential in Asean Industrial Companies Using Altman Z-Score and Springate Methods

This research aims to analyze the potential for financial distress in the industrial sector companies across five ASEAN countries, using the Altman Z-score (1993) and Springate (1978) methods. Subsequently, these estimations are utilized to determine the error rate in classifying companies into specific groups, such as failed, safe, and grey-area companies. To measure the significance of differences among these groups, an independent sample t-test is employed. The bankruptcy analysis results from the Altman Z-Score and Springate methods within the industrial sector companies across ASEAN countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand) in 2018 until 2020 indicate varying levels of financial distress.

Analisis Potensi Kebangkrutan Dengan Mengunakan Model Springate S.Score

Analysis of Potential Bankruptcy Using the Springate S.Score Model (Case Study) in Aviation Companies Listed on the IDX.This research aims to determine financial distress in airline companies listed on the stock exchange. The type of data used is secondary data in the form of data in the form of documents obtained from the official BEI link (www.idx.co.id). The data analysis technique used is the Springate model. .score. The research results show the springate score analysis method. This is indicated by the Springate score being less or more than the predetermined score. in airline companies during 2018-2021, (1) PT Garuda Indonesia (Tbk) (2) PT Jaya Trishindo (Tbk) (3) PT MNC energy investment (Tbk) and (4) PT AirAsia (Tbk) experienced financial distress, and in 2022 it will also experience financial distress but PT Garuda Indonesia will not experience financial distress.

Developing System of Indicators Estimating Bankruptcy Possibility for Public Catering Organizations within the Frames of Crisis Management

Developing the optimum crisis strategy depends mainly on the quality of analytical base, which is built in the process of finance analysis and diagnostics of organization bankruptcy. It is connected with organizations of public catering as well. Today issues of shaping the optimum set of indicators to estimate bankruptcy possibility have not been clarified so far. Thus the necessity of investigations in the field is stipulated by debatable nature of accounting for sectoral peculiarities in the current assessment methods. The goal of the research is to identify specific features of building the system of indicators for estimating bankruptcy possibility in public catering organizations. Such research methods as comparative analysis, statistic analysis, correlation analysis, bench-marking, classification and ranking were used, as well as the modified method of hierarchy analysis. As a result of the research academic and practical recommendations were put forward dealing with developing the system of indicators for estimating bankruptcy possibility in public catering organizations. The authors came to the conclusion that the optimum system of indicators for estimating bankruptcy possibility could foster the development of efficient strategy of crisis management.

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.

Analysis of Potential Bankruptcy with The Springate Method in Oil and Gas Subsector Companies Listed on The IDX 2018-2021

Changes in world oil prices are a things that must be considered, because they can affect the selling price of crude oil and the profits to be obtained. Apart from that, the rupiah exchange rate against the US dollar is also one thing that needs attention. These conditions become one of the considerations for investors whether the results of the investment can provide profits or have a loss. One of the problems that must be considered is financial distress. The aims of this research is to use the Springate method to analyze the potential for bankruptcy in oil and gas subsector companies. This research uses a quantitative descriptive approach and data collection uses documentation techniques by taking secondary data from the website of the Indonesia Stock Exchange. The sample was selected using certain sampling criteria, so that the sample in this study amounted to 10 companies. The results of the analysis of potential bankruptcy using the springate method in 2018 to 2020 show that there are 8 companies in the distress zone and 2 companies in the safe zone. In 2021 there are 9 companies in the distress zone and 1 company in the safe zone.

Analisis Kebangkrutan PT. Prasidha Aneka Niaga Tbk Menggunakan Metode Altman Z-Score Periode 2016 - 2020

Bankruptcy analysis by PT. Prasidha Aneka Niaga Tbk 2016-2020 uses the Altman Z-Score method. The purpose of this research is to find out whether a company is experiencing bankruptcy in the current period so that corrective actions can be taken at an early stage. This study used a quantitative descriptive method and documents for data collection. He gathered data from company records after what was needed to explain the research questions. The analytical method used is the Altman Z-Score method. According to the survey results, the company's financial ratios achieved from 2016 to 2020 have increased in 2017 and 2018 after experiencing financial difficulties in 2016, this indicates that it is not experiencing financial difficulties but is in a stable condition. However, in 2019 and 2020, it will once again face financial difficulties.

Bank efficiency evaluation and bankruptcy analysis model

Bank efficiency evaluation and bankruptcy analysis model

Analisis kebangkrutan pada PT. Garuda Indonesia Tbk. Dengan Menggunakan Model Springate

Every business always tries to make a profit, so the financial health of the business needs to be maintained so that it runs well. However, companies must be able to foresee the possibility of bankruptcy. Bankruptcy analysis is carried out to get early warning of bankruptcy. One of the tools used to predict the potential for bankruptcy of a company is the Springate S-Score method. The purpose of this research is to find out the financial performance of PT. Garuda Tbk for the 2019-2022 period and if there is a potential for bankruptcy. This research method uses qualitative. Data collection techniques with documentation studies. The analysis technique uses the Springate model. The results obtained, especially from 2019 to 2022, will experience fluctuations. In 2019, the company PT. Garuda Tbk is included in the healthy category, in 2020 and 2021 it will be included in the type prone to bankruptcy. In 2022, the company will be better and can enter the healthy group.